Bibliographic record
Abstract
What part do physical environments play in materializing relationships? How is intimacy created in specific places, through specific objects? What do these places and objects imply about our understandings of intimacy? To answer these questions, I draw on fieldwork I conducted in Québec, Canada, and mobilize the tools of ethnography and qualitative social research to create a methodological bricolage with the aim of making familiar cultural processes seem strange (Clifford 1986). My project of making intimacy “strange” draws from queer studies and materiality and affect theories, which aim to deconstruct heteronormativity and to de-center the focus on humans, based on the work of Bruno Latour (2005), Tim Ingold (2012), Judith Butler (2002) and Sara Ahmed (2004). I use the material as a point of entry to understand how intimacies are performed and subtly mediated by elements that go unnoticed in common sense definitions of intimacy. From this perspective, intimacy is created by a combination of things: humans, objects, spaces, affect and texts that are each more or less attached to collective and personal aspirations. My contribution lies in showing how some objects and places are involved in the performativity of intimacies, and how the material can create ephemeral intimacies and concretize relationships. The cases I work through indicate a capacity for the (re)creation of intimacy, the reproduction of normative ideas, as well as a kind of distributed agency through the multiplicity of things that are necessary to achieve intimacy.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".