Platform Capitalism and the Governance of Knowledge Infrastructure
Bibliographic record
Abstract
This is the slides for the closing keynote at the Digital Initiative Symposium, held at the University of San Diego, April 29-30, 2019. Abstract: The dominant academic publishers are busy positioning themselves to monetize not only on content, but increasingly on data analytics and predictive products on research assessment and funding trends. Their growing investment and control over the entire knowledge production workflow, from article submissions, to metrics to reputation management and global rankings means that researchers and their institutions are increasingly locked into the publishers’ “value chain”. I will discuss some of the implications of this growing form of “surveillance capitalism” in the higher education sector and what it means in terms of the autonomy of the researchers and the academy. The intent is to call attention to the need to support community-governed infrastructure and to rethink our understanding of “openness” in terms of consent and social values. Special thanks to Giulia Forsythe (Associate Director, Centre for Pedagogical Innovation. Brock University, Canada) for the illustration on slide#24, "This is not a healthy 'ecosystem" of knowledge."
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".