Seethe post introducing this sequencefor context, caveats, credits, and links to prior discussion relevant to this sequence as a whole. This post doesn’t necessarily represent the views of my employers.
In a previous post, I highlighted some observations that I think collectively demonstrate that the current “pipeline” for producing EA-aligned research and researchers is at least somewhat insufficient, inefficient, and prone to error. In my next three posts, I’ll describe several possible interventions to improve the pipeline, such as running research training programs, increasing and improving EAs’ use of non-EA options for research training and credentials (e.g., PhD programs),[1] and creating a database of information on research project ideas.
But first, in this post, I’ll discuss some goals we might want to have in mind when designing, evaluating, and/or implementing interventions to improve the pipeline. These goals could be roughly split into three categories:
Goals focused on improving aspiring/junior researchers’ expected future impact, via helping them with:
Building knowledge, skills, etc.
Network-building
Testing fit
Gaining credible signals of fit
Career planning
Goals focused on improving the EA-aligned research pipeline, via helping with:
Learning about the intervention(s)
Providing mentorship/management training
Getting resources for the intervention
Other goals:
Direct impact (of the research produced as a direct, near-term result of the intervention)
Increasing awareness of and inclination towards EA-relevant things (among the aspiring/junior researchers, their “mentors”, or other people)
Enjoyment (among people interacting with the intervention)
Avoiding downside risks
(Note that some of those “Other goals” are partly about improving aspiring/junior researchers’ expected future impact and/or improving the EA-aligned research pipeline, but they don’t focus primarily on just one of those things.)
Target audience and purpose of this post
As with the rest of this sequence:
This post is primarily intended to inform people who are helping implement or fund interventions to improve the EA-aligned research pipeline, or who could potentially do so in future
But it may also help people who hope to themselves “enter” and “progress through” the EA-aligned research pipeline
I think there are two ways this post could be useful for such people:
Reading this list of goals may help make the precise nature of the problem clearer, by providing an alternative, complementary framing to that given in my previous post.
Having these goals and some commentary on them written down could help ensure people don’t forget to consider some of them, and could help people think about how much they care about each goal relative to the other goals.
This in turn seems useful for the generic reason that one is likely to come up with different intervention ideas—and implement them differently—depending on what goals one has in mind and prioritises. (See the end of the post for two examples relevant to this context.)
Caveats
When you’re currently designing, evaluating, and/or implementing an intervention for improving aspects of the EA research pipeline, you should of course also think for yourself about what goals are relevant to your specific situation.
And you should also probably consider doing things like conducting interviews or surveys with potential “users” or “experts”.
This post is adapted from a list of possible goals I thought of for a particular research training program; it’s possible I would’ve come up with a different list if, for example, I’d been thinking of other types of interventions from the start, or had gathered more systematic data on what various groups’ needs are
Some of the goals I mentioned overlap in a way that might cause confusion or create a risk of double-counting the benefits of one solution relative to another.
I’d welcome pushback or additional ideas regarding my list, labels, or descriptions.
Possible goals
Goals focused on improving aspiring/junior researchers’ expected future impact
Building knowledge, skills, etc.
Meaning: Allowing the aspiring/junior researchers to build knowledge, skills, habits, mindsets, etc. that help them do other impactful things later.
(This includes skills like “research intuitions” or “research taste” and habits/mindsets like proactiveness and self-direction.)
Network-building
Meaning: Allowing or helping aspiring/junior researchers to build connections with each other, with mentors (where relevant), and with other people they interact with in relation to the intervention.
These connections could help aspiring/junior researchers or those they’re connected with in finding projects to do, having someone to vouch for them, executing projects (e.g., via exchanging info on some topic), etc.
Testing fit
Meaning: Allowing aspiring/junior researchers to test their own fit (passion, skills, etc.) for research careers, specific fields, specific topics, etc.
Something that could fit either here or in “Knowledge and skills” is helping aspiring/junior researchers have the confidence (where actually warranted) to apply for things, tackle projects productively without too much second-guessing or risk of burnout, etc.
Gaining credible signals of fit
Meaning:
Making it easier for aspiring/junior researchers to get future roles/projects that they should get
Maybe making it less likely that aspiring/junior researchers get future roles/projects that they shouldn’t get
This seems less important, but might in some cases be beneficial for both the researcher and for other people
Reducing the time cost required by others in order to find or vet aspiring/junior researchers for roles/projects
Hiring rounds can be expensive and can fail to include potentially great candidates
Ways an intervention could achieve those goals include:
Allowing an aspiring/junior researcher to produce outputs that signal their skills, interests, etc.
Increasing the chances that a researcher’s outputs actually reach relevant audiences and influence their decisions, which could help with signalling, allow the researcher to have references for applications, etc.
Allowing mentors or others to get a sense of a researcher’s skills, interests, etc., beyond the info which the output alone provides
These people could then serve as references for the researcher or could themselves hire provide the researcher with a job or funding
Maybe allowing an aspiring/junior researcher to try to produce outputs that would signal their skills, interests, etc., such that, when some of them fail to produce such outputs, this serves as more meaningful evidence that they shouldn’t be given the role/project
Career planning
Meaning: Providing aspiring/junior researchers with time, structures/guidance, encouragement/nudges, and feedback for career planning.
Goals focused on improving the EA-aligned research pipeline
Learning about the intervention(s)
Meaning:
Gaining valuable info about things like how useful a given intervention is for improving the pipeline, how to make it better, and whether it’d be worth making variants of it for certain specific purposes (e.g., specifically for PhD students, specifically for non-academic work, or specifically for people who aren’t part of the EA community).
Gaining practice implementing a given intervention, such that one could do so more effectively in future.
Providing mentorship/management training
Meaning: Allowing mentors/managers to develop their knowledge, skills, and confidence in mentoring/managing others.
This could occur as a result of things like the mentors/managers getting more experience in those roles, being provided with tips and resources, or going through formal training.
This could help them better mentor/manage other people in future, either as part of the same intervention, at the org they work for, or elsewhere.
Getting resources for the intervention(s)
The most notable resources for many relevant interventions are probably funding and mentor time.[2]
Pursuing this goal could include:
Making it sufficiently clear that the intervention is sufficiently promising that it’s worth providing the funding necessary to allow the intervention to continue to exist or be improved
Maintaining good relationships with mentors, and ensuring the aspiring/junior researchers they’re paired with will seem promising and like good fits for that mentor
(Of course, achieving this goal is only a good thing if the intervention really is sufficiently promising to warrant these resources!)
Other goals
Direct impact
Meaning: The impact that results from the research outputs that the researcher produces as a direct result of the intervention (e.g., while participating in a research training program), as opposed to the impact that comes later and indirectly (e.g., via the researcher being more skilled or getting a more impactful job).
Increasing people’s awareness of and inclination towards EA-relevant things
Meaning: Increasing awareness of, inclination towards, and engagement with EA or particular EA-relevant causes, interventions, etc., in order to allow for benefits like more donations, better allocated donations, better career plans, or more and better EA-aligned research produced by non-EAs.
These changes in awareness, inclination, and engagement could occur among:
the aspiring/junior researchers reached by the intervention
their “mentors”
other people who interact with or observe those people, their outputs, or the intervention
For example, a well-run research training program that involves high-quality events and produces fairly high-quality research outputs might also increase mentors’ and observers’ inclination towards EA and the cause areas the program focused on.
This goal is very related to “movement building”. Part of this goal could be referred to as “improving non-participants’ views and priorities”.
Meaning: Increasing demographic and/or cognitive diversity[3] in EA as a whole, in specific orgs, or in the communities of people working on high-priority issues.
Demographic and cognitive diversity are quite distinct things, and actions that improve one kind of diversity will not necessarily improve the other. But often both goals could achieved via the same types of interventions, such as interventions aimed at:
Recruiting/training new EA-aligned researchers who are from demographic groups that aren’t currently well represented in EA or in the relevant orgs/communities, or whose perspectives, thinking styles, etc. aren’t currently well-represented in those places
Risks to the reputation or relationships of EA or of important organisations/cause areas/communities
Hard-to-reverse harms to the future career prospects of the aspiring/junior researcher[5]
Causing people to focus too much or too little on research roles and/or roles at explicitly EA orgs
(For further discussion of accidental harm/downside risks in general, see here.)
Two examples of how different goals could push in different directions
These are just two examples, and just for illustration.
Example 1
The more weight we put on the goal of “Experimentation with the intervention(s)”, the more we should try a range of interventions, try lots of different specific choices within each intervention, gather data, write reflections as we go, share our reflections with other people afterwards, etc.
Example 2
Imagine we’re choosing which of the following to do:
Things like guiding aspiring/junior researchers towards working on especially high-priority topics for their undergraduate or graduate theses
Things like guiding aspiring/junior researchers towards getting great general research training or working under excellent research supervisors (even if those supervisors work on less relevant topics)
Typically, we should probably focus more on the former type of thing the more weight we put on the goals of “Increasing people’s awareness of and inclination towards EA-relevant things” and “Direct impact”, but more on the latter type of thing the more weight we put on “Building knowledge and skills”.
As noted earlier, I’d welcome pushback or additional ideas regarding my list, labels, or descriptions.
In light of these goals, my next post will overview 19 intervention options improving the EA-aligned research pipeline.
I’m using the term “EAs” as shorthand for “People who identify or interact a lot with the EA community”; this would include some people who don’t self-identify as “an EA”.
Other resources could include advisors and “advocates” for the intervention itself. (Advocates could include people who just encourage other people to use the intervention, faculty members who help provide official university endorsement for a research training program, etc.)
Additionally, one could aim to increase the chance that a given person maintains (parts of) the perspectives, thinking styles, etc. that they have which are somewhat uncommon in EA. For example, one could try to reduce pressures or incentives favouring intellectual conformity, or could encourage people to form and share their independent impressions.
For convenience, I’ll sometimes lump various different types of people together under the label “aspiring/junior researchers”. I say more about this group of people in the previous post of this sequence.
Goals we might have when taking actions to improve the EA-aligned research pipeline
See the post introducing this sequence for context, caveats, credits, and links to prior discussion relevant to this sequence as a whole. This post doesn’t necessarily represent the views of my employers.
In a previous post, I highlighted some observations that I think collectively demonstrate that the current “pipeline” for producing EA-aligned research and researchers is at least somewhat insufficient, inefficient, and prone to error. In my next three posts, I’ll describe several possible interventions to improve the pipeline, such as running research training programs, increasing and improving EAs’ use of non-EA options for research training and credentials (e.g., PhD programs),[1] and creating a database of information on research project ideas.
But first, in this post, I’ll discuss some goals we might want to have in mind when designing, evaluating, and/or implementing interventions to improve the pipeline. These goals could be roughly split into three categories:
Goals focused on improving aspiring/junior researchers’ expected future impact, via helping them with:
Building knowledge, skills, etc.
Network-building
Testing fit
Gaining credible signals of fit
Career planning
Goals focused on improving the EA-aligned research pipeline, via helping with:
Learning about the intervention(s)
Providing mentorship/management training
Getting resources for the intervention
Other goals:
Direct impact (of the research produced as a direct, near-term result of the intervention)
Increasing awareness of and inclination towards EA-relevant things (among the aspiring/junior researchers, their “mentors”, or other people)
Increasing demographic and/or cognitive diversity
Enjoyment (among people interacting with the intervention)
Avoiding downside risks
(Note that some of those “Other goals” are partly about improving aspiring/junior researchers’ expected future impact and/or improving the EA-aligned research pipeline, but they don’t focus primarily on just one of those things.)
Target audience and purpose of this post
As with the rest of this sequence:
This post is primarily intended to inform people who are helping implement or fund interventions to improve the EA-aligned research pipeline, or who could potentially do so in future
But it may also help people who hope to themselves “enter” and “progress through” the EA-aligned research pipeline
I think there are two ways this post could be useful for such people:
Reading this list of goals may help make the precise nature of the problem clearer, by providing an alternative, complementary framing to that given in my previous post.
Having these goals and some commentary on them written down could help ensure people don’t forget to consider some of them, and could help people think about how much they care about each goal relative to the other goals.
This in turn seems useful for the generic reason that one is likely to come up with different intervention ideas—and implement them differently—depending on what goals one has in mind and prioritises. (See the end of the post for two examples relevant to this context.)
Caveats
When you’re currently designing, evaluating, and/or implementing an intervention for improving aspects of the EA research pipeline, you should of course also think for yourself about what goals are relevant to your specific situation.
And you should also probably consider doing things like conducting interviews or surveys with potential “users” or “experts”.
This post is adapted from a list of possible goals I thought of for a particular research training program; it’s possible I would’ve come up with a different list if, for example, I’d been thinking of other types of interventions from the start, or had gathered more systematic data on what various groups’ needs are
Some of the goals I mentioned overlap in a way that might cause confusion or create a risk of double-counting the benefits of one solution relative to another.
I’d welcome pushback or additional ideas regarding my list, labels, or descriptions.
Possible goals
Goals focused on improving aspiring/junior researchers’ expected future impact
Building knowledge, skills, etc.
Meaning: Allowing the aspiring/junior researchers to build knowledge, skills, habits, mindsets, etc. that help them do other impactful things later.
(This includes skills like “research intuitions” or “research taste” and habits/mindsets like proactiveness and self-direction.)
Network-building
Meaning: Allowing or helping aspiring/junior researchers to build connections with each other, with mentors (where relevant), and with other people they interact with in relation to the intervention.
These connections could help aspiring/junior researchers or those they’re connected with in finding projects to do, having someone to vouch for them, executing projects (e.g., via exchanging info on some topic), etc.
Testing fit
Meaning: Allowing aspiring/junior researchers to test their own fit (passion, skills, etc.) for research careers, specific fields, specific topics, etc.
Something that could fit either here or in “Knowledge and skills” is helping aspiring/junior researchers have the confidence (where actually warranted) to apply for things, tackle projects productively without too much second-guessing or risk of burnout, etc.
Gaining credible signals of fit
Meaning:
Making it easier for aspiring/junior researchers to get future roles/projects that they should get
Maybe making it less likely that aspiring/junior researchers get future roles/projects that they shouldn’t get
This seems less important, but might in some cases be beneficial for both the researcher and for other people
Reducing the time cost required by others in order to find or vet aspiring/junior researchers for roles/projects
Hiring rounds can be expensive and can fail to include potentially great candidates
Ways an intervention could achieve those goals include:
Allowing an aspiring/junior researcher to produce outputs that signal their skills, interests, etc.
Increasing the chances that a researcher’s outputs actually reach relevant audiences and influence their decisions, which could help with signalling, allow the researcher to have references for applications, etc.
Allowing mentors or others to get a sense of a researcher’s skills, interests, etc., beyond the info which the output alone provides
These people could then serve as references for the researcher or could themselves hire provide the researcher with a job or funding
Maybe allowing an aspiring/junior researcher to try to produce outputs that would signal their skills, interests, etc., such that, when some of them fail to produce such outputs, this serves as more meaningful evidence that they shouldn’t be given the role/project
Career planning
Meaning: Providing aspiring/junior researchers with time, structures/guidance, encouragement/nudges, and feedback for career planning.
Goals focused on improving the EA-aligned research pipeline
Learning about the intervention(s)
Meaning:
Gaining valuable info about things like how useful a given intervention is for improving the pipeline, how to make it better, and whether it’d be worth making variants of it for certain specific purposes (e.g., specifically for PhD students, specifically for non-academic work, or specifically for people who aren’t part of the EA community).
Gaining practice implementing a given intervention, such that one could do so more effectively in future.
Providing mentorship/management training
Meaning: Allowing mentors/managers to develop their knowledge, skills, and confidence in mentoring/managing others.
This could occur as a result of things like the mentors/managers getting more experience in those roles, being provided with tips and resources, or going through formal training.
This could help them better mentor/manage other people in future, either as part of the same intervention, at the org they work for, or elsewhere.
Getting resources for the intervention(s)
The most notable resources for many relevant interventions are probably funding and mentor time.[2]
Pursuing this goal could include:
Making it sufficiently clear that the intervention is sufficiently promising that it’s worth providing the funding necessary to allow the intervention to continue to exist or be improved
Maintaining good relationships with mentors, and ensuring the aspiring/junior researchers they’re paired with will seem promising and like good fits for that mentor
(Of course, achieving this goal is only a good thing if the intervention really is sufficiently promising to warrant these resources!)
Other goals
Direct impact
Meaning: The impact that results from the research outputs that the researcher produces as a direct result of the intervention (e.g., while participating in a research training program), as opposed to the impact that comes later and indirectly (e.g., via the researcher being more skilled or getting a more impactful job).
Increasing people’s awareness of and inclination towards EA-relevant things
(See the Awareness/Inclination Model in How valuable is movement growth?)
Meaning: Increasing awareness of, inclination towards, and engagement with EA or particular EA-relevant causes, interventions, etc., in order to allow for benefits like more donations, better allocated donations, better career plans, or more and better EA-aligned research produced by non-EAs.
These changes in awareness, inclination, and engagement could occur among:
the aspiring/junior researchers reached by the intervention
their “mentors”
other people who interact with or observe those people, their outputs, or the intervention
For example, a well-run research training program that involves high-quality events and produces fairly high-quality research outputs might also increase mentors’ and observers’ inclination towards EA and the cause areas the program focused on.
This goal is very related to “movement building”. Part of this goal could be referred to as “improving non-participants’ views and priorities”.
Increasing demographic and/or cognitive diversity
Meaning: Increasing demographic and/or cognitive diversity[3] in EA as a whole, in specific orgs, or in the communities of people working on high-priority issues.
Demographic and cognitive diversity are quite distinct things, and actions that improve one kind of diversity will not necessarily improve the other. But often both goals could achieved via the same types of interventions, such as interventions aimed at:
Recruiting/training new EA-aligned researchers who are from demographic groups that aren’t currently well represented in EA or in the relevant orgs/communities, or whose perspectives, thinking styles, etc. aren’t currently well-represented in those places
Retaining such people in such places[4]
See also posts tagged Diversity and Inclusion.
Enjoyment
Meaning: Aspiring/junior researchers—and their “mentors”, where relevant—having a pleasant experience.
Avoiding downside risks
Relevant downside risks could include:
Information hazards
Risks to the reputation or relationships of EA or of important organisations/cause areas/communities
Hard-to-reverse harms to the future career prospects of the aspiring/junior researcher[5]
Causing people to focus too much or too little on research roles and/or roles at explicitly EA orgs
(For further discussion of accidental harm/downside risks in general, see here.)
Two examples of how different goals could push in different directions
These are just two examples, and just for illustration.
Example 1
The more weight we put on the goal of “Experimentation with the intervention(s)”, the more we should try a range of interventions, try lots of different specific choices within each intervention, gather data, write reflections as we go, share our reflections with other people afterwards, etc.
Example 2
Imagine we’re choosing which of the following to do:
Things like guiding aspiring/junior researchers towards working on especially high-priority topics for their undergraduate or graduate theses
Things like guiding aspiring/junior researchers towards getting great general research training or working under excellent research supervisors (even if those supervisors work on less relevant topics)
Typically, we should probably focus more on the former type of thing the more weight we put on the goals of “Increasing people’s awareness of and inclination towards EA-relevant things” and “Direct impact”, but more on the latter type of thing the more weight we put on “Building knowledge and skills”.
As noted earlier, I’d welcome pushback or additional ideas regarding my list, labels, or descriptions.
In light of these goals, my next post will overview 19 intervention options improving the EA-aligned research pipeline.
I’m using the term “EAs” as shorthand for “People who identify or interact a lot with the EA community”; this would include some people who don’t self-identify as “an EA”.
Other resources could include advisors and “advocates” for the intervention itself. (Advocates could include people who just encourage other people to use the intervention, faculty members who help provide official university endorsement for a research training program, etc.)
“Cognitive diversity has been defined as differences in perspective or information processing styles” (Reynolds & Lewis, 2017)
Additionally, one could aim to increase the chance that a given person maintains (parts of) the perspectives, thinking styles, etc. that they have which are somewhat uncommon in EA. For example, one could try to reduce pressures or incentives favouring intellectual conformity, or could encourage people to form and share their independent impressions.
For convenience, I’ll sometimes lump various different types of people together under the label “aspiring/junior researchers”. I say more about this group of people in the previous post of this sequence.