Social computing as social rationality
Bibliographic record
Abstract
This project concerns the ways in which social computing functions as a rational steering medium in network societies. Exploring cases that include the structured data protocols of an ascendant "Web 3.0", Google PageRank and collaborative filtering services, the work unearths some key intellectual commitments at work in the technologies. Each software structure constructs a kind of social rationality, by combining the lived experience of users with its rationalizing computational processes. The cases have been chosen as among those digital tools increasingly relied upon to coordinate action in everyday life: organizing people and knowledge in diverse ways, recalibrating the operations of large bureaucracies and institutions, serving as new feedback mechanisms for the network economy, and functioning as novel formats for everyday communication between friends, family and citizenry. To help compare the cases, the project outlines several philosophical forms of rationality. Doing so helps in turn to highlight three aspects of social computing: how certain conditions of epistemic validity and successful action are being encoded into software algorithms and protocols; how each case rationally models the achievement of consensus, via some configuration of the semantics and pragmatics of language, and finally, how each case enrolls distributed social participation to potentiate the conditions of its operation.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".