Hearing the inaudible experimental subject: Echoes of Inaudi, Binet’s calculating prodigy.
Bibliographic record
Abstract
Historians of psychology have traditionally focused on ideas (intellectual history), the "great men" who produced them (an older style of biography sometimes called "hagiography"), or-more recently-the influence of the contexts that shaped them (social and cultural history). A still more recent approach is to bring in those invisible subjects whose experiences have previously been ignored, most often through histories focusing on the discipline's forgotten women or minority contributors: "history from below" (subaltern history). A variation on this was popularized in the history of psychiatry (viz., "patient voices") and has since been carried into the history of psychology (e.g., "feminist voices"). The latest innovation is to focus on what Jill Morawski has referred to as "the discipline's experimental subjects." (These are the collective done-to, rather than the doers, of psychological research.) This history is one of those: an attempt to look behind Alfred Binet to find an influence that shaped his work. The purpose is thus to "give voice" to this unheard-from subject-the until-now inaudible Jacques Inaudi (including excerpts from newspaper interviews and translations from his recently discovered autobiography)-and at the same time advance Morawski's historiographical project. We then get a glimpse of what it was like to be a child prodigy in France in the 1880s, as well as what securing scientific patrons could do for one's prospects. By focusing specifically on Binet's unheard-from experimental subject, we are also afforded new perspectives of the history of late-19th century French psychology (reflecting another emerging interest, "international history"), and we gain new insights into the prehistory of contemporary Binet-style intelligence testing.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".