‘Were they nice people? Were they asking good questions?’: Searching for an ethics of love in the history of communication research
Bibliographic record
Abstract
The history of the field of communication has often forgotten and erased marginalized voices, contributions, and experiences of certain knowers, consolidating a predominantly exclusionary historiography. Women researchers in particular have been erased from the history of the field. We argue that these exclusions have helped construct a monolithic and masculinized understanding of our field, not only in terms of our canon and referents, but also regarding hegemonic epistemic practices and perspectives. Through eight in-depth interviews with prominent second-generation (1960–1990s) women researchers in the field of communication from Australia, Brazil, Canada, France, Italy, the United States, and the United Kingdom, this article tries to recover some of the alternative approaches and epistemic practices that have remained disregarded in the field. The article proposes the widespread existence, in the history of communication and media research, of a counter-hegemonic approach to epistemic practices and relationships, one that is shaped by cooperation, affection, dialogic relationships, and, ultimately, by an ethics of love.
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 | 0.036 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.122 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".