From the language of learning to the language of educational responsibility
Bibliographic record
Abstract
How do I recognise the move from the language of learning to the language of educational responsibility? ….each practitioner researcher clarifies, in the course of their emergence, in the practice of educational enquiry, the embodied ontological values to which they hold themselves accountable in their professional practice. (Whitehead J (2005) Living inclusional values in educational standards of practice and judgement. Keynote for the Act, Reflect, Revise III Conference, Brantford Ontario, 11 November 2005. Available at: http://www.jackwhitehead.com/monday/arrkey05dr1.htm (accessed 29 August 2014).) I have spent the last two years exploring my values and beliefs in the classroom and working with the children in my class to develop our skills and reflections as learners. As educational researchers we need to go beyond the language of learning into a language of educational responsibility; a responsibility to ourselves and the children, to generate educational explanations and evaluations of our practice as we try to live our values in our class. This paper shows how I move from the language of learning to a language of education in clarifying the meaning of the educational responsibility I feel towards the children in my care.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.094 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".