Social Science and Social Struggle: Understanding the Necessary Confluence of Scholarship and Political Commitment
Bibliographic record
Abstract
Social scientists and historians are wary to acknowledge that political commitments play a part in their explanations of society. But we all know they do. Are we poor scientists? Not according to the Edinburgh School, which argues all successful scientific theories are but practical knowledge, shaped by the encounter of human purpose and empirical world. Practical knowledge always involves the uncertain, trial and error application of the intellectual resources drawn from exemplary solutions to new situations. Praxis is the only valid path to knowledge. But no matter how successful, practical knowledge is a theoretically and empirically limited ‘working knowledge' which cannot produce sure understanding of the generative processes producing what we see. What distinguishes studies of society from those of Nature is that the political purposes of conflicting scholarly traditions are so deeply and manifestly divergent. Implications? Above all we should be skeptical about any strong claims to theoretical certainty, on our part or by others. Dogmatism and sectarianism are epistemologically untenable in the Edinburgh view. Scientific debate would be advanced if we were as open about our political orientations as we are enjoined to be about research design and methodology. And demanded the same of others. This may be possible across ‘camps' in the same tradition and even ideological barriers, where goodwill prevails. In the public sphere the Edinburgh perspective suggests the shifting of the grounds of debate and the framing/reframing of issues requires a tacit recognition that social knowledge is shaped by its political purposes and cannot simply be ‘the facts m'am, just the facts.'
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.016 | 0.177 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".