Bibliographic record
Abstract
If any of you have ever read the popular book by Nicholas Carr titled, "Does IT Matter", you know that he is being intentionally provocative in challenging us to redefine how we invest in Information Technology. In his book he talks about proprietary and infrastructural technology, and lumps most IT into latter. The premise is that the true value of IT is not fully realized until it is broadly shared, homogenized and standardized. In essence, we lose our ability, or need, to differentiate ourselves and we are at a point where innovation on an individual/institutional level will not lead to a meaningful advantage. That does not preclude this innovation, it simply states that the true value lies in sharing the innovation. Although his book is geared towards the private sector, there are many parallels to the world of Higher Education and certainly the financial realities are prominent. Faced with inevitable budget constraints, the maturation of DDI, and advances in other technologies the need to articulate a shared vision and act collectively becomes imperative. In this part of the panel we will talk about commoditization of IT and how we painted a vision for shared development, resources and access with the ODESI project.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".