Bibliographic record
Abstract
I think I've read too many mission statements.Or, more to the point, I've read the same mission statement too many times.These days, each mission statement promises an education of quality.I have yet to see an institution aspire to mediocrity, although the word mediocre leaps to mind for a good number of institutions that claim to provide a "quality education."In fact, I don't ever want to hear the words "quality education" again.Nor do I want to hear about "excellence," "high measurable academic standards," or "skills for a changing world."I don't care much for the words "rigour," "exceptional programs and facilities" or "commitment to the global community" either.These phrases have lost whatever meaning they might once have had, and perhaps even more important, measures of so-called success for such goals as "excellence" or "commitment to the global community" are often wanting.Indeed, the overall feeling that one is left with, after reading such mission statements, is that they are cold, that they dull our collective capacity to live and learn with joy.I have, on occasion, read mission statements that have caused me to pause.For example, the College of Fine and Applied Arts at the University of Illinois at Urbana-Champaign lists as one of its goals (after the inevitable "quality education and teaching excellence") "the creation of visual art objects, music compositions and performances, theatrical productions, dance choreography and repertory, and ... the design of sustainable architecture, landscapes and plans that nurture communities."This goal, among others, is in
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".