The cultural change work of change agents without formal authority: Integrating sustainability into an organization’s culture
Bibliographic record
Abstract
I would like to acknowledge the extremely generous support of my supervisory committee.Steph, thank you so much for taking me on board.You were always there for me, through the good and the bad, and through the personal and the professional.You are a true role model for how to conduct meaningful work, live authentically, and make positive change around you every day.Jen, thank you for always being there for me as I wrestled with my analysis and contribution.Your boundless enthusiasm, positivity, and energy are contagious and helped make research a fun journey of exploration for me.Brent, thank you for your constant support and friendship throughout the last four years.You helped me think critically about my work and introduced me to perspectives and ideas that had a profound impact on how I see the world today.I am indebted to all three of you, and you have all been model supervisors that I hope to emulate one day
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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".