Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper critically interrogates the viability of “Queer” as an ontological category, identity, and radical political orientation in an era of digital surveillance and Big Data analytics. Drawing on recent work by Matzner (2016) on the performative dimensions of Big Data, I argue that Big Data’s potential to perform and create Queerness (or its opposites) in the absence of embodiment and intentionality necessitates a rethinking of phenomenological or affective approaches to Queer ontology. Additionally, while Queerness is often theorized as an ongoing process of negotiations, (re)orientations, and iterative becomings, these perspectives presume elements of categorical mobility that Big Data precludes. This paper asks: what happens when our data performs Queerness without our permission or bodily complacency? And can a Queerness that insists on existing in the interstitial margins of categorization, or in the “open mesh of possibilities, gaps, and overlaps” (Sedgwick 1993: 8), endure amidst a climate of highly granular data analysis?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it