Bibliographic record
Abstract
In this article, I mount a defense of objectivity as a fitting and necessary norm for the conduct of social scientific research. A number of social scientists and philosophers have criticized this norm because it seems to call for disinterested investigations that are free from any kind of evaluative judgments and seems overwhelmingly to favor quantitative research. I argue that these criteria are inappropriately used as guidelines for objectivity. Researchers can comply with the norm of objectivity, rightly understood, and still be interested observers, make value judgments in relation to their research, and conduct qualitative studies. I argue instead that the norm of objectivity refers to a set of guidelines for interpreting and reporting on research that views this reporting as an intelligible, reasonable, and inherently reciprocating, public activity. By implication these norms also establish correlative guidelines for gathering and analyzing research information. Briefly, as investigators social scientists are called upon honestly to represent our research, to use measures and terms of references that allow for comparisons and verifications by our audiences, and to exercise responsible judgments. I conclude that my account of objectivity is consistent with Weber’s defense of this norm more than a century ago.
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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 | 0.263 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.182 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.016 | 0.032 |
| 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".