Bibliographic record
Abstract
This paper will examine the role of affect as a key force in the experience of reading. I argue reading is an experience rather than an instrument of cognition. The latter, I suggest, gives way to dehumanizing and violating forms of reading as instrument or nomenclature or categorization. I suggest that new relations can be made in, within and from practices of reading that conceive it as a fragile, faltering practice of knowledge production. My reconceptualization of reading derives from Derrida’s notion of deconstruction as learning to read the trace of affect unraveling the normative and/or binary procedures cohering privileged and/or prejudicial understandings of the text. Reading for affect asks researchers to engage in an analysis of the felt and non-evident of people, events and texts. Rather than comprehend in the sense of cognition, we feel the trace of meanings cathected in signs before we know what the sign might mean. My paper will expand on reading as conveying affectivity or the unheard embedded in text. Increasingly there is a demand to read the other’s words with greater attention to feeling for what they do and do not say, can and cannot speak. In studies that purport to do research with humans, it seems to be imperative to engage in a practice of reading that attends to the other meanings indicated by words. This requires the development of a critical or close research reading practice that attends to affect or the interior meanings of articulations and text that words cannot easily convey.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".