Bibliographic record
Abstract
In 2014, at the height of gamergate hostilities, a blockbot was developed and circulated within the gaming community that allowed subscribers to automatically block upwards of 8,000 Twitter accounts. "Ggautoblocker" as it was called, was designed to insulate subscribers' Twitter feeds from hurtful, sexist, and in some cases deeply disturbing comments. In doing so it cast a wide net and became a source of considerable criticism from many in the industry and games community. During this time, the International Game Developers Association (IGDA) 2015 Video Game Developer Satisfaction Survey was circulating, resulting in a host of comments on the blockbot from workers in the industry. In this paper we analyze these responses, which constitute some of the first empirical data on a public response to the use of autoblocking technology, to consider the broader implications of the algorithmic structuring of the online public sphere. First, we emphasize the important role that ggautoblocker, and similar autoblocking tools, play in creating space for marginalized voices online. Then, we turn to our findings, and argue that the overwhelmingly negative response to ggautoblocker reflects underlying anxieties about fragmenting control over the structure of the online public sphere and online public life. In our discussion, we reflect upon what the negative responses suggest about normative expectations of participation in the online public sphere, and how this contrasts with the realities of algorithmically structured online spaces.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".