Bibliographic record
Abstract
This paper investigates the intersection of big data and philosophy of education by considering big data’s potential for addressing learning via a holistic process of coming-to-know. Learning, in this sense, cannot be reduced to the difference between a pre- and post-test, for example, as it is constituted at least as much by qualities of experience as it is the situation, process of inquiry and its consequences. Long a perennial concern of philosophers of education, the author suggests that big data offers a budding opportunity for philosophers to engage in dialogue with empirical research in order to better understand the process of learning as coming-to-know. Drawing on John Dewey’s theory of inquiry and his philosophy of experience, the author demonstrates ways that both empirical and philosophical research stands to benefit from cross-dialogue. In offering an unprecedented glimpse of empirical detail, the author proposes that big data stands to afford new insights into this most complex human process and that Dewey’s philosophy offers a vital lens of interpretation that can help philosophers of education to make use of this data in addressing the perennial question of how humans come-to-know.
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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 | 0.059 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.018 | 0.055 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.019 | 0.046 |
| 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".