MétaCan
Menu
Back to cohort
Record W3045922015 · doi:10.7916/cjtl.v11i2.6841

ENHANCING EFFICIENCY AT NONPROFITS WITH ANALYSIS AND DISCLOSURE

2020· article· en· W3045922015 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueColumbia Journal of Tax Law · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsInefficiencySubsidyProfitability indexIncentiveBusinessGovernment (linguistics)Profit (economics)MarketingEconomicsPublic economicsPublic relationsFinanceMarket economyMicroeconomicsPolitical science

Abstract

fetched live from OpenAlex

The U.S. nonprofit sector spends $2.54 trillion each year. If the sector were a country, it would have the eighth largest economy in the world, ahead of Brazil, Italy, Canada, and Russia. The government provides nonprofits with billions in tax subsidies, but instead of evaluating the quality of their work, it leaves this responsibility to nonprofit managers, boards, and donors. The best nonprofits are laboratories of innovation, but unfortunately some are stagnant backwaters, which waste money on out-of-date missions and inefficient programs. To promote more innovation and less stagnation, this Article makes two contributions to the literature. First, this Article breaks new ground in identifying sources of inefficiency at nonprofits. The literature focuses on incentives, arguing that managers and board members are less motivated to run a nonprofit efficiently because they cannot keep its profits. In response, this Article emphasizes that the problem is not just motivation, but also information. Measuring success is harder at nonprofits. Instead of tracking profitability, they use metrics that are less reliable and harder to measure. These measurement challenges complicate the efforts even of dedicated and competent managers to operate efficiently. While this information problem is familiar, another has been largely overlooked in the literature: When success is hard to measure, incompetence and self-interested practices are less visible, and thus are harder to stop. For example, if managers regularly overpay vendors, the consequence at a for-profit firm (lower profits) is easier to observe than at a nonprofit (less effective service for beneficiaries).  Second, this Article recommends a response to this underappreciated source of inefficiency: better analysis and disclosure as a strategy for organizational change. In principle, nonprofits are supposed to maximize social return, but how can they operationalize this abstract principle? To help them do so, this Article recommends three questions that nonprofits should answer every year: first, how important are the challenges the nonprofit is trying to address?; second, how effective are the nonprofit’s responses to these challenges?; and third, is the nonprofit the right organization to respond to these challenges? These questions press nonprofit managers and boards to be more explicit about priorities, monitor progress, improve and expand high-value programs, and fix or shut down ineffective ones. This Article also recommends that nonprofits should disclose this analysis to the public, even though current law does not require them to do so. This disclosure would empower donors and rating agencies to be more effective monitors. It also would help donors make better informed philanthropic choices and would enable charities to borrow innovative ideas from each other more easily.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.201
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it