Bibliographic record
Abstract
J o 11rnal of Ed11 ca ti ona l Tho ug ht, Vo l. 30, No. 3, December, I 996 309 familial abuses occur, many edu cators and storyte ll ers foc us on th e hap py ending these tales give children , enco urag ing the children to accep t th e way thin gs are.Zip es 's pro gramme spea ks out aga in st thi s silence and sil encin g.Unlike neo-Jungian ch ampions o f" storytellin g such as Carli ssa Pink ola Es tes and Rob ert B ly, Zip es takes a c lea r view of story as ne ith e r brin gin g abo ut utopi a nor maintaining the st atu s q uo.For him , story is onl y as produ cti ve as th e community from whi ch it deri ves , a community willin g to grow and ch ange and questi on and constantl y manoeuvre its indi viduals into p os iti ons in whi ch th ey can expose the contradi c ti ons inh erent within soc ial in stituti ons.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".