Research and Technology Organizations as Super Intermediaries: A Conceptual Framework for Policy and a Case Study From Tanzania
Bibliographic record
Abstract
Research and Technology Organizations (RTOs) have key roles in stories of national industrial development in many countries, and in various contexts they have transformed according to changes in their surrounding economic and policy environments. This paper proposes a conceptual framework of 'RTOs as super intermediaries' as they play multiple intermediary roles in the triple helix (government, research and industry), the overlap of industrial policy and research policy, and research-industry frontiers. The framework helps in understanding and advancing the role of RTOs in industrial development, particularly in developing countries. For a case study, the paper showcases research in Tanzania that explored possibilities of revamping RTOs and whether investing in them would help in spurring Tanzania's industrial development. Through key informant interviews and systemic literature review, a case study on the challenges and opportunities of RTOs was designed to examine their role and potential in industrial development and technology innovation processes. The study findings were overall in-line with two main lenses of inquiry: 1) that for RTOs to play their key roles in Tanzania, industrial policies shaped by the command economy era before the 1990s need to be reviewed and modified; and 2) that more investment in revamping RTOs will take place if policymaking processes acknowledge RTOs as super intermediaries. To organize policy lessons drawn, a multi-level policy map-micro, meso and macro-was utilized as an analytical tool.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".