Language and Power: Ascribing Legitimacy to Interpretive Research
Bibliographic record
Abstract
More than merely describing what constitutes a good or truthful interpretation, all judgments about the legitimacy of knowledge claims can be understood as enacting relations of power. That is, our understanding of what it means to make a reasonable claim to knowledge is already caught up in relations of power that privilege some perspectives and marginalize others. Language, understood as productive rather than reflective of meaning, both enables and constrains the kinds of statements we are entitled to make. Competing discourses do not exist equally in the world but rather differ in terms of what they are held to explain and what effect they have. The authors explore these issues and suggest that evaluating interpretive research involves not only epistemological issues but also questions of value and power.
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 | 0.240 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.015 | 0.280 |
| Scholarly communication | 0.047 | 0.067 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.013 | 0.015 |
| 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".