Developing Inclusive Innovations to Address Institutional Failures
Bibliographic record
Abstract
The panel session will support a conversation among leading scholars studying inclusive innovation and institutional theories. We aim to do so by bringing together researchers interested in enhancing our understanding about organizations’ strategies to solve unstructured problems that affect disenfranchised populations. In particular, this class of problems is usually present in institutionally flawed settings, which have ‘voids in commercial institutions’ (Dutt et al., 2016). These institutional misalignments can be expressed in terms of weak property rights, less developed capital markets, less established formal job markets, and higher risk of poverty vulnerability. To that end, the panel’s goal is to improve our knowledge of how inclusive organizations might help overcome institutional misalignments in low-income locations, fostering socioeconomic development. We believe that the panel is aligned with the main theme of the 2019 Academy of Management Conference, “Understanding the Inclusive Organization,” in particular with the following divisions: Strategic Management (STR); Entrepreneurship (ENT); and Social Issues in Management (SIM).
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".