meeting youngsters where they “are at” in summer camps, in sport and in life.
Bibliographic record
Abstract
When Mathew Lipman first introduced Philosophy for Children (P4C) to the world, his goal was not to sneak a little academic philosophy into the typical school curriculum, as one might expect from the titles of his first books: Philosophy in the Classroom (Lipman et al., 1980) and Philosophy Goes to School (Lipman, 1988). His goal, rather, was to create a paradigm shift in the field of education itself: namely, to transform the typical hierarchical model into one in which the teacher/facilitator solicits responses from students and hence, in that sense, meets them where they “are at.” This non-hierarchical model, however, has stumbled in taking root, which is, perhaps, not surprising given that the hierarchical model, whether in school, in sport or in the home, appears to be so much easier and so much more efficient. If those of us who support a non-hierarchical model in all these arenas are serious about furthering this approach, it would appear that the onus lies with us to articulate precisely in what ways a hierarchical model falls short. In so doing, we will not only provide ourselves with a precise framework by which to make the case for the importance of adopting a non-hierarchical approach, we will also provide ourselves with a metric whereby we can measure whether our own non-hierarchal practice is true to its justification; and that the approach is not simply non-hierarchal for sake of being non-hierarchical, nor quasi-authoritarianism for the sake of more wide-spread acceptance. It is the articulation of the flaws of a hierarchical model that non-hierarchal model can (and should) correct that will be the focus of the analysis here
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".