A regression model explaining predisposition to collaborate
Bibliographic record
Abstract
[First paragraph of Introduction]: In Canada the last two decades of the 20th century have been characterized by government policies that focus on stimulating the economy as a strategy for improving overall quality of life. This Ahard right turn@ has made tax cuts a priority over social program expenditures, and private sector efficiencies have been promoted as the most effective response to financial challenges (Jeffrey, 1999; McBride & Shields, 1997; Tester, 1996). Voluntary organizations in Canada have had to adapt to this new environment. Both federal and provincial governments have been withdrawing from direct service provision in several areas of social welfare with the expectation that the voluntary sector will fill any resulting gaps in the social safety net. At the same time, a decrease in government support for the voluntary sector has limited its capacity to respond to an increased demand for its services (Hall & Banting, 2000). In Canada, on average 64% of revenues for voluntary organizations have come from government grants and payments (Hall & Macpherson, 1997). The federal government limited transfer payments for various social programs (Tester, 1996), and as a result, the provinces began a systematic retrenchment of these programs (Torjman, 1996). With such intense dependence on the government, any change in transfer payments is bound to have a noticeable impact on the sector (Rice & Prince, 2000). This paper focuses on interorganizational activities among voluntary organizations as a response to the funding changes being experienced by the sector. Specifically, we develop a model that presents collaborative behaviour as a function of organizational characteristics, environmental pressures and organizational attitudes. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".