Feminist cybernetic, critical race, postcolonial, and crip propositions for the theoretical future of Human-Machine Communication
Bibliographic record
Abstract
The authors review theoretical trends in HMC research, as well as recent critical interventions in the HMC journal that usefully reshape and expand our research terrain. Conventional research such as positivist and quantified approaches are identified as restraining research questions and delimiting understandings of concepts including subjects, agency and interactivity. Feminist cybernetic, critical race, postcolonial and crip theoretical approaches are offered, examining how they fill research gaps in HMC, expanding content areas explored, and addressing diverse intersectional pressures, situated, and time/space dynamics that impact human machine interaction. The authors suggest these shifts are essential to expanding HMC research to address diverse populations, regional realities around the globe, and engage in vibrant scholarly debates occurring outside HMC. They contend these shifts will outfit HMC to weigh in on important issues of justice, equity, and access that arise with emerging technologies, climate change, and globalization dynamics.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.071 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".