Bibliographic record
Abstract
Getting from Montreal to Makassar is not a picnic. During the thirty-six hours my partner and I spend in transit, we debate whether it is more important to teach public health or philosophy in Indonesia, because this is the reason for our three-week trip to the capital of the Indonesian province of Sulawesi. We both teach at McGill University: my partner is a medical doctor, specializing in public health; I'm a historian of philosophy, working, among other things, on Muslim and Jewish thought. The classes we give at Alauddin State Islamic University—one of fourteen academic institutions in Indonesia that make up the public system of Islamic higher education under the auspices of the ministry of religious affairs—are part of a McGill-based Indonesia Social Equity Project, funded by the Canadian International Development Agency (CIDA). Nobody denies the usefulness of teaching medicine and public health, especially in a developing country. But why does CIDA send a philosopher instead of a second doctor or, for that matter, a social worker, an engineer, or an economist? Someone, in other words, whose expertise is of immediate use for improving the living conditions of Indonesians? Most people—in Indonesia and elsewhere—don't even know that the problems philosophers turn over in their minds exist. Much less do they feel the need to understand or resolve them. Are their lives any less happy for that reason? Many would say that the opposite is the case.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".