Bibliographic record
Abstract
school, or give this one more time and encouragement to improve performance? As a school head, should I apply the same discipline for this student as I would for any other, or should I take into account circumstances that might warrant making an exception? As a classroom teacher, how can I balance the extra time one of my students needs, with the fast pace that keeps the rest of my students attentive and interested? As a student, should I choose to report the dangerous behavior of my friend, even though I swore myself to secrecy? Really tough ethical dilemmas come about when core values conflict. The task of determining the “higher right” can be daunting. How can we respect short term needs that seem pitted against other, longer term responsibilities? Does fairness always trump compassion, or are there times when it’s more ethical to bend the rules? How can I balance my responsibilities to an individual if they conflict with the needs of a larger group? When does my regard for the truth outweigh my responsibility to confidentiality? Everyone in a school community is faced at times with this kind of values-based decision making. So everyone can benefit from paying attention to ethics as both an urgent literacy requirement for our students, and as a unifying force in developing positive school culture. Both those reasons lay behind the founding of The Institute for Global Ethics (IGE), an international, non partisan and non sectarian charity with offices in Canada, England and the U.S. IGE’s president, Rushworth M. Kidder, learned about the urgency of this issue as one of the first Western journalists to cover the Chernobyl reactor disaster. He was deeply alarmed that, as he says, “before there was a nuclear meltdown, there had been a moral meltdown.”
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".