<i>Keywords for Children's Literature</i> : mapping the critical moment
Bibliographic record
Abstract
As Anglophone co-editors of Keywords for Children's Literature, we (Philip Nel and Lissa Paul) were delighted to be invited as keynote speakers for the Nordic Children's Literature Conference in Oslo. When we gave our live talk in August 2012, we staged it is as a performance, a dialogue accompanied by PowerPoint images. Because our performance would not work on the page, we composed a print version of our talk to read both as a coherent prose narrative, and as a reflection on ways in which we were shaped by the occasion. In keeping with one of the central mandates of the conference—“to serve as a meeting point for new research environments”—we decided to open our talk on Keywords for Children's Literature by positioning it in the research environments in which it began. We later explained how the book “works,” and concluded by projecting ways in which we might situate it in emerging research environments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".