ALGORITHMIC PRODUCTION BEYOND SILICON VALLEY
Bibliographic record
Abstract
The last years have seen a proliferation of research on the social ramifications of algorithms (Eubanks 2018; Noble 2018) and the power of algorithms was insightfully theorized (Gillespie 2016; Bucher 2018). At the same time, scholars have begun to examine the ties between algorithms and culture (Seaver 2017), describing algorithms as products of complex socio-algorithmic assemblages (Gillespie 2016, 24), with often very local socio-technical histories (Kitchin 2017). However, the spatial trajectories through which algorithms operate, and the specific sociocultural contexts in which they arise have been largely overlooked. Accordingly, research tends to focus on American companies and on the effects their algorithms have on Euro-American users, while, in fact, algorithms are being developed in various geographical locations, and they are being used in diverse socio-cultural contexts. That is, research on algorithms tends to disregard the heterogeneous contexts from which algorithms arise and the effects various cultural settings have on the production of algorithmic systems. This panel aims to fill these gaps by offering four empirical perspectives on algorithmic production in three prominent tech centers: China, Canada, and Israel. We will ask: How do cross-cultural encounters construct notions of privacy? How is algorithmic discrimination understood and acted upon in China? What symbolical and material resources were invested in making Canada’s AI hubs? And how Israeli tech companies use their algorithms to profile their Other? Hence, this panel offers to think beyond the Silicon Valley paradigm, and to aim towards a more diverse, culturally-sensitive approach to the study algorithms.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".