DIS[S]CURSE[IF] CHALLENGES: PROFESSIONAL CONVERSATIONS IN CHILD AND YOUTH CARE IN FLUID MODERNITY
Bibliographic record
Abstract
I have, as you can see, entitled my talk: Dis[s]curse[if] Challenges: Professional Conversations in Child and Youth Care in Fluid Modernity. I chose this title, in part, with a smile on my face. We have, in the last few years, in what we sometimes refer to as the child and youth care “academy”, challenged ourselves to work with language forms that signal our knowledge of, and affection for, the history, the geneology, of the ideas that support our practices and also show that we can speak the vernacular. New turns of phrase, new ways of employing words that for many of us have had fixed meanings, now force us to meet these words again in different guises, and so create the need for a re-acquaintance, for hearing and seeing again in a different way, what we thought we knew: Words move [and] music moves… Words after speech, reach into the silence… Words strain, crack and sometimes break, under the burden, Under the tension, slip, slide, perish, Decay with imprecision, will not stay in place, Will not stay still. Shrieking voices Scolding, mocking or merely chattering, Always assail them…(Eliot, 1944, p. 19)
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.045 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".