What Can Be Accomplished in 25 Years: In Memoriam of Dr. Stafford Hood
Bibliographic record
Abstract
Dr. Stafford Hood left this earthly realm on Sunday, January 15th, 2023. His sudden passing created ripples of grief and rumination throughout the evaluation community. Hood was an evaluation and assessment expert, scholar, practitioner, teacher, and innovator. Last year, 2023, marked the 25th anniversary of his talk “Responsive Evaluation Amistad Style,” where he introduced the term “culturally responsive evaluation.” There have been a plethora of tributes to him (see the Hood Obituary, Center for Culturally Responsive Evaluation and Assessment [CREA] Statement, University of Illinois, CREA–Dublin, M. Q. Patton Video, American Evaluation Association Blog). Thus, as a board member of the Canadian Journal of Program Evaluation, CREA affiliate faculty member, and mentee of Dr. Hood, the author humbly offers some brief professional and personal reflections on his legacy 1 year after his passing. How much time is needed to uplift unheard voices, rectify years of erasure, change theory, shift praxis, connect scholars, and mentor the next generation? Dr. Stafford Hood accomplished all of this and transformed the landscape of our field in a mere 25 years. He introduced a revolution, rectified years of erasure, created a movement, and solidified his legacy.
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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".