"What's the catch?" Testing theories regarding the implications of recent federal initiatives for the social sciences and the humanities
Bibliographic record
Abstract
There has been subtantial academic concern over what is deemed to be the negative implications of the recent "reinvestment" phase of federal research funding (1999 to 2008). For the social sciences and humanities, however, nuch of this concern is not substantiated with any actual data. This thesis examines five of these proposed theses (or "fears") and finds that only one can be supported by actual evidence. The remaining four concerns are thus premature or erroneous. The first thesis centers around total dollar funding amounts and argues that federal funding is either dwindling for the social sciences and humanities or has decreased in proportion to other disciplines. Using publicly available data this is found to be false and in conflict with the actual trends occurring during this period. The second thesis argues that federal funding initiatives specifically target business or industrial-related research, to the detriment of the public good. This fear is qualified and then dismissed through an analysis of private sector R&D expenditures, commercialization activity, and new initiatives developed during this period. Related to the above, the third thesis posits that federal funding has become "targeted," or directed away from basic research and towards specific (applied) fields of federal interest. Using public data this is shown to be false. Thesis four constitutes the only fear which is substantiated in this analysis. Scholars have argued that recent funds have cultivated a certain level administrative control over the direction of faculty research, primarily for the purposes of increasing funding success. This trend is examined in detail in chapter 3 using interview data with research officials at Canadian institutions. The final thesis argues that the value of social sciences and humanities grant success is growing for both universities and faculty and has led to a corresponding increase in competition, hierarchy, and differentialization between Canadian institutions. After examining this argument in chapter 4, this hypothesis is found to be in serious need of qualification. The work concludes by noting that the net effects of funding fluctutations on research activity for these disciplines are much less pervasive than most scholars recognize. It also argues that greater intellectual rigor is needed if future publications actually expect to help academics understand changes on this issue. Quantitative and qualitative changes in research funding do have serious implications for the research activity of the disciplines at large. Unfortunately, to date, these implications have neither been well described nor accurately represented by the scholars devoted to their exposition.
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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 | 0.080 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".