The Case for Asymmetry in Online Research: Caring About Issues in Australian and Canadian Web 1.0 Bee Networks
Bibliographic record
Abstract
We critically engage with the actor–network theory precept that human and nonhuman actants have symmetrical capacities. In contrast, we distinguish actor-actants, who have the capacity to care about other actants, from issue-actants, who do not. We explore the gathering of participants leading to the emergence of matters of concern by mapping how Australian and Canadian bee-related websites connect to the issue of bee extinction (“colony collapse”). A “symmetrical” hypothesis was that major differences in local geographies and exposure to parasites would result in different rates of connection. This hypothesis was confirmed: All influential Canadian actor-actants connected to “colony collapse,” whereas no influential Australian actor-actants did. Our findings also suggest an “asymmetrical” interpretation: Influential Australian actor-actants were aware of the catastrophic disappearance of bees, but did not care. Denying that some actants have agency over others means that it is impossible to form a moral opinion about connections or about the rights of dominated actor-actants.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.032 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".