Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<JATS1:p>From the global geopolitical arena to the smart city, control over knowledge—particularly over data and intellectual property—has become a key battleground for the exercise of economic and political power. For companies and governments alike, control over knowledge—what scholar Susan Strange calls the knowledge structure—has become a goal unto itself.</JATS1:p> <JATS1:p>The rising dominance of the knowledge structure is leading to a massive redistribution of power, including from individuals to companies and states. Strong intellectual property rights have concentrated economic benefits in a smaller number of hands, while the “internet of things” is reshaping basic notions of property, ownership, and control. In the scramble to create and control data and intellectual property, governments and companies alike are engaging in ever-more surveillance.</JATS1:p> <JATS1:p>This open access book is a guide to and analysis of these changes, and of the emerging phenomenon of the knowledge-driven society. It highlights how the pursuit of the control over knowledge has become its own ideology, with its own set of experts drawn from those with the ability to collect and manipulate digital data. Haggart and Tusikov propose a workable path forward—knowledge decommodification—to ensure that our new knowledge is not treated simply as a commodity to be bought and sold, but as a way to meet the needs of the individuals and communities that create this knowledge in the first place.</JATS1:p> <JATS1:p>The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Social Sciences and Humanities Research Council of Canada (SSHRC)</JATS1:p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it