Trust in the system: an introduction to the #AoIR2019 special issue
Bibliographic record
Abstract
This special issue of Information, Communication and Society reflects on the generative work presented at the 2019 annual conference of the Association of Internet Researchers (#AoIR2019). The conference attracted approximately 380 people from 35 countries to work through the theme, Trust in the System. Delegates analysed developments on the internet, in social media and through data management, including those grounded in Indigenous perspectives and varied communities, resiliencies and collective voices. Plenary provocations prompted discussions about our various relationships with “trust”, “system” and “the”, while research sites included webtoons and webnovels, fans and games, chemsex and porn, the rise of digital assistants and evolving digital practices in politics, health, education, environment and the media. Creative industries, automation and platformization figured broadly. Ethics, methods and theory ranged from science and technology studies (STS) to queer and indigenous theory to algorithmic approaches, digital ethnography, creative methods, and emergent work in bot detection across social media. The resulting articles curated for this collection are offered by emerging to established scholars, from around the world.
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 | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".