Bibliographic record
Abstract
… problems are solved, not by giving new information, but by arranging what we have always known. (Wittgenstein, 1953, aphorism 109) There have been curious rumblings of late in the social sciences and the helping professions that draw from them. Increasing numbers of academics have called into question the notion that the social sciences could do for the helping professions what the natural sciences have done for engineering or biomedicine. Even the language used for articulating such notions has become suspect, as communications theorists and linguistic philosophers have turned our most fundamental social reality – being in conversation with each other – into a matter of critical reflection. At the same time, larger cultural issues have come to the fore. Where are women and minority culture people represented in the so-called universal knowledge of ‘man’ to be applied in helping others? The very foundations of what seemed a secure knowledge base have been under assault. New helping practices, and ways of thinking about them, have been emerging that look positively anarchic compared to orderly helping protocols and received social scientific knowledge about humans, and humans in interaction. The ideals of enlightenment science, applied to human endeavours and concerns, if you go along with the critics, come up short in delivering the equivalents of the kinds of understandings and practices that get bridges built, or people on the moon.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.311 | 0.187 |
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".