Utilization of Research Findings and their Role in Research Management as an Interdisciplinary Field
Bibliographic record
Abstract
Research management has been brought up as an interdisciplinary subject –a combination of “management” and “research methodology”- especially in Humaninties and Social Sciences research. As an essential concept in research management, utilization of research findings is so important that some believe neglecting it would cause failure of research efforts. Utilization Model is the frame within which the matter is programmed. Introducing different Utilization models, the present study attempts to suggest a variety of research frameworks to be selected in proportion to organizational status. Points of strength and weakness of any model have been explored here. Three different uses –instrumental, conceptual, and procedural- have been explained according to their corresponding utilization models. In the procedural use, various models are introduced including communication models, science push, knowledge driven, problem solving, demand push, diffusion, stetler, the knowledge-to action process, cronbach Rossi model in conceptual use, and the Ottawa model, Canadian Institutes of health research, understanding user context framework, collaborative model, and the utilization forced model for evaluation. They are introduced to be selected for different organizational structures. The variety of utilization models show that they are especially rooted in the local environment, and thus when applying them, the user should pay attention to the specific characteristics of the models besides the organizational and environmental specifications.
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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 | 0.421 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.031 | 0.021 |
| Science and technology studies | 0.011 | 0.077 |
| Scholarly communication | 0.048 | 0.044 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".