Confronting Descriptive-Prescriptive Paradoxes in the Pursuit of Equity, Justice, and Knowledge
Bibliographic record
Abstract
Management scholars can advance theoretical insight with significant practical implications by identifying when and why status quo procedures are failing to satisfy intended goals, and develop theoretically- informed solutions. The scholars in this symposium document descriptive- prescriptive paradoxes when confronting systemic barriers to equity, justice, and knowledge accumulation. In doing so, the speakers not only contribute novel theoretical insights that advance the field’s understanding of individual and system-level goal pursuit, but also demonstrate clear practical implications for individuals and organizations to better align their actions with their goals. Striving to survive or to thrive? Motives for aid seeking in the context of SNAP Author: Ilana Brody; University of California Los Angeles Author: Sherry Jueyu Wu; University of California Los Angeles Author: Eugene M. Caruso; University of California Los Angeles Author: Heather M. Caruso; University of California Los Angeles Advancing DEI or imposing diversity work? The pernicious effects of misguided representation goals Author: Jacob W. Roberson; USC Marshall School of Business, University Of Southern California Author: Leigh Plunkett Tost; University of Southern California Spoken softly, heard less: The gendered use of passive voice in workplace complaints Author: Grusha Agarwal; University of Toronto Author: Sonia Kang; University of Toronto An empirical test for how well the literature on behavioral interventions generalizes Author: Linnea Gandhi; The Wharton School, University of Pennsylvania Author: Elizabeth Tipton; Northwestern University Author: Duncan J. Watts; University of Pennsylvania
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.103 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".