Landwash Readers: A Space of Collective Reading in the Medical Humanities
Bibliographic record
Abstract
As readers, the sedimentations of our surrounding world ensure that we never read alone. There is an uneasy otherness in all readings, through which naming our obstacles is a bringing to the fore a consciousness of lack and flatness; what Maxine Greene calls an achievement of freedom in education as a transcendence of the given, an overcoming that is never complete. Obstacles in learning, which signify the dialectical nature of every human situation, are stirrings that engage dialogue in the place of an assumed silence, imagining the possible pluralities of subjectivity through learning on the verges of a fractured space. Within the context of this pedagogical confrontation, I examine the articulations of a reading group in the medical humanities from St. John’s, Newfoundland. Surpassing the closed nature of individual readings, such groups choose to act out their freedoms, and in doing so, interact with other people and textual forms as obstacles, where reading is a looping, a struggle, and a risking of free choice in a landspace that is forever shifting. As a commitment of an inherently social nature, Greene’s struggle for freedom in learning enables an embracing of alterity in a reader’s process of becoming; to become different from what one is, and what one is supposed to be—pursuing a curriculum of incessant rupturing, where openings and cleavages are always available. In this paper, I look at one use-value of social reading shared by members of this reading group: Resistance, and a Legitimation of Artistry in Medicine.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.060 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| 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".