The Politics of Interface: A Comparative Reading of Concordia University Library's Discovery Search Interface and the Sci-Hub Interface
Bibliographic record
Abstract
This research project consists of a comparison of the search interfaces of two different online resource-finding tools: Concordia University Library (specifically, its discovery search) and Sci-Hub. My goal is to examine how specific elements of these sites articulate what I am terming different "politics of interface." I have deliberately chosen both "legitimate" and "piratical" (or shadow) search interfaces, as I examine how context shapes the politics of interface for each finding tool. Specific to the shadow library context, I aim to explore what differences an interface can reveal about the "textual community" that uses it and that community's information access needs. How does this contrast with our interactions with, or search queries of, institutional interfaces? For example, in an institutional context, is a textual community of users as easily identifiable? Finally, how does all of this speak to the ways in which a search interface can position its user as both a consumer and producer of knowledge?
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".