Bibliographic record
Abstract
Quentin Skinner’s work is central to Intellectual History. This article reviews his methodological critiques and exegetical recommendations as they can be of interest for historians of education, educational comparatists, and analysts of educational policies. The article analyzes Skinner’s proposals against alternative methodologies. Firstly, it explores the criticisms he makes to traditional forms of the History of Ideas. These criticisms attack, in an original way, well known fallacies that are also common in History of Education. Secondly, the article explores his constructive proposals based in linguistic philosophy. The article introduces Skinner’s idea of “meaning,” which focuses on the intention of the author in issuing or writing the utterance or text. This intention can be worked out by considering the linguistic-rhetoric context of the work. The focus on intentions and contexts, which puts at the center of the exegesis what was considered a fallacy among positivist historians, implies a change in the conception of History of Education and in its relation to Philosophy of Education. The article sketches, as a way of conclusion, some implications of these constructive proposals for the work of comparatists, historians and philosophers of education. DOI: http://dx.doi.org/10.15572/ENCO2014.05
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".