Bibliographic record
Abstract
The concept of the “public good ” is open to interpretation. A public good could be thought of in contrast to a private good, thus, suggesting this topic is about things that could be public or private. Instead it is the idea of “goodness ” that guides this author’s interpretation. One may wish to consider the principle of being guided by what one deems to be working out of a spirit of goodness. This suggests to me a striving that goes beyond meeting the requirements of a position to extend into practices and actions that are motivated by serving a greater capacity than one’s own. Perhaps it could be thought of as making a societal contribution that is well intentioned and meaningful beyond the immediacy of the situation in which it is offered. The aforementioned introduction is personal. The definition of good work is contextually personal in this respect. My inclination to use the word striving articulates this perception as it is through striving that the goodness is achieved. Further it is not critical to my understanding of education for the public good that you necessarily agree with this perception. I would go as far as to say that this negotiation and reconfiguring of perceptions is essentially part of striving itself. Rigidity stands in the way of this
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".