I just wrote a bit more about how we measure IASPCs in another comment on this thread. We don’t use a precise formula and the details are important so I can’t say exactly how we’d rate a particular change at this level of generality.
That said, we take into account someone’s degree of follow through when we score their plan change, such that very few of our highest rated plan changes (rated-100 or more) are from people who are not currently doing impactful work.
30% have changed their plans but not yet passed a major “milestone” in their shift. Most of these people have applied to a new graduate programme but not yet received an offer.
30% have reached a milestone, but are still building career capital (e.g. entered graduate school, or taken a high-earning job but not yet donated much).
40% have already started having an impact (e.g. have published research, taken a non-profit job).
I agree if we didn’t take follow through into account it would lead to some scores that were far removed from expected impact such as the hypothetical you’ve described.
Hi Jan,
I just wrote a bit more about how we measure IASPCs in another comment on this thread. We don’t use a precise formula and the details are important so I can’t say exactly how we’d rate a particular change at this level of generality.
That said, we take into account someone’s degree of follow through when we score their plan change, such that very few of our highest rated plan changes (rated-100 or more) are from people who are not currently doing impactful work.
Of the rated 10’s, our analysis in 2017 found:
30% have changed their plans but not yet passed a major “milestone” in their shift. Most of these people have applied to a new graduate programme but not yet received an offer.
30% have reached a milestone, but are still building career capital (e.g. entered graduate school, or taken a high-earning job but not yet donated much).
40% have already started having an impact (e.g. have published research, taken a non-profit job).
I agree if we didn’t take follow through into account it would lead to some scores that were far removed from expected impact such as the hypothetical you’ve described.
Hope this clarifies things.