Open Content Alliance (OCA) vs. Google Books: OCA as superior network and better fit for an emerging global public sphere
Bibliographic record
Abstract
The Open Content Alliance (OCA) is a network of libraries and similar organizations committed to digitizing and providing broadest possible access to books and other materials; over 1.6 million books are already online under OCA principles. OCA is analyzed in contrast with Google Books (as per the preliminary Google Books Settlement, November 2009), using Castell’s network theory and theories of an emerging global public sphere, based on the work of Habermas and Fraser. OCA is seen as a superior network to Google Books, with particular strengths in connectedness, consistency (shared goals), flexibility, scalability, survivability, networking (inclusion / exclusion) power, and network-making power, including the ability to form strategic alliances. The lawsuit against Google Books, and the settlement, illustrate some of the limitations of Google Books as a network, for example the lawsuit per se is a challenge to Google Books’ rights to make decisions on inclusion and exclusion, and illustrates poor connectedness and consistency, two attributes Castells points to as essential to the performance of a network. The respectful, law-abiding approach of OCA is a good fit for a global public sphere, while the Google Books Settlement takes a key issue that has traditionally been decided by governments (orphan books), and brings the decision-making power into private contract negotiations, diminishing democracy. The current Google Books Settlement is fractured on a national (geographic) basis; consequences could include decreased understanding of the rest of the world by a leading nation, the U.S. This works against the development of a global public sphere, and has potential negative economic and security implications for the U.S.. OCA is presented as one node of an emerging library network for the global public sphere, a global public good increasing access to knowledge everywhere, increasing the potential for informed public debate towards global consensus.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".