Government Support and Infrastructure: Realizing the value of collaborative work
Bibliographic record
Abstract
CCommunity-campus research has undergone significant growth over the last two decades. While there has been some support in the form of government programs, significant gaps remain. The identification of collaborative research – what Gibbons et al. (1994) called Mode Two, complementing more traditional Mode One research – necessitates a better understanding of the incentives and infrastructure needed to produce greater value from both modes of research production. This article presents an argument that research is fundamentally three questions: what, so what and now what. It further argues that while the system is good at producing data and information as well as interpretation and analysis, it is not quite so competent when it comes to decisions that produce value beyond products, programs and sometimes, policies. This article introduces concepts related to knowledge mobilization and the need for dedicated incentives and infrastructure to realize the value of collaborative work. It introduces a taxonomy of legal government powers to protect and promote public health that may be adapted to the creation of support for community-campus research. This article suggests that government support for collaborative research must be built from arguments that demonstrate the added value that comes from engaging in these processes. It further argues that this is essentially a political process that must include explicit and open conversations across sectors and stakeholders.
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 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 | 0.018 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".