Bibliographic record
Abstract
This article explores technology fetishism in academic libraries as an irrational form of worship. Academic libraries participate in networks of prestige through their investments in technology and its fetishistic rhetoric. To counter the myth of technology as a neutral good, this article draws on contemporary fetishism theory and specifically the work of Bruno Latour to trace how technology is entangled with social relations and upholds hegemonic power. All technology is laden with human thought, feeling, and intent. However, Modern fetishes are dispersed into culture and obscure these entanglements, hiding materiality and obscuring the visibility of labour. This article considers library technology through the lens of fetishism, specifically considering the ways in which discovery layers shape research. Confronting fetishism enables academic library workers to reimagine more human-centered approaches to technology and to bring to light embedded whiteness and sexism in library practices. There is an urgent need to reconfigure our relationships with technology given its entanglement with research and the unexamined power that fetishism holds.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.020 | 0.084 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.001 | 0.033 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".