Bibliographic record
Abstract
In the September 2003 issue, Meckler and Baillie correctly argued that social constructionists need not deny the importance of truth or objectivity. This comment probes their understanding of those two concepts. The view that truth entails correspondence with the facts, although not false, is not helpful. Our understanding of truth must be able to encompass the truth of normative claims and counterfactuals and of “deeper” truths. Meckler and Baillie’s view that an objective statement is one that is independent of our beliefs is also challenged. All statements depend on our beliefs but these beliefs are themselves more or less plausible and self-evident. An objective statement can thus be understood as one whose background conditions are viewed as reasonable or self-evident. Various implications follow: Objectivity is a matter of degree, and one can reasonably speak of the objectivity of our norms as much as the objectivity of our fact claims.
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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".