Introduction: Metaphors as Meaning and Method in Technoculture
Bibliographic record
Abstract
Metaphors are critical sites of analysis for feminist scholars of science and technology because of what they both conceal and divulge about the conditions of their historical emergence and the persistence of those conditions. As researchers and editors, we find ourselves oriented to work that takes up the task of contesting uncontested metaphors, considering how metaphor “invades” (Tuck & Yang 2012, 3) and evacuates meaning. This Special Section carries on the dynamic practice in feminist STS of taking the work, and ambivalent potentiality, of metaphor seriously. In this Introduction, we draw together scholarship that informs what we identify as the "metaphor-work" of feminist STS—the work of allegory, myth, metaphor, figurative and associative discourse, and their analysis—as central to the methods by which we make and remake meanings that matter to feminist technocultures. Throughout the metaphor-work collected here, the contributors propose that paradigm change comes through the collective refusal of some metaphors, through the re-evaluation of others, and the introduction of new metaphorical frames and figures to reorient our work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".