A theory of change approach to analysing tourism social entrepreneurship research
Bibliographic record
Abstract
This paper advances a Theory of Change (ToC)-based framework to assess the fragmented literature on Tourism Social Entrepreneurship (TSE), an emerging field centred on inclusive, place-based social transformation through tourism. Moving beyond conventional systematic literature reviews, it proposes a five-step, conceptually guided approach to assess how existing studies contribute to the impact pathway: from context and inputs to outputs, outcomes, and impacts. The review surfaces explanatory insights and persistent oversights including two cross-cutting issues: the black box problem (theoretical opacity around mechanisms) and the attribution problem (methodological ambiguity in causal inference). By foregrounding assumptions and tracing mechanisms, the ToC framework offers a structured, strongly conceptualised, replicable approach to deepen explanation and guide future research in TSE and other impact-oriented domains.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".