The Philosophy and Practice of Interpretivist Research in Entrepreneurship
Bibliographic record
Abstract
Knowledge production in entrepreneurship requires inclusivity as well as diversity and pluralism in research perspectives and approaches. In this article, the authors address concerns about interpretivist research regarding validity, reliability, objectivity, generalizability, and communicability of results that militate against its more widespread acceptance. Following the nonfoundationalist argument that all observation is theory-laden, context specific, and that there are no external criteria against which to assess research design and execution and the data produced, the authors propose that quality must be internalized within the underlying research philosophy rather than something to be tested upon completion. This requires a shift from the notion of validity as an outcome to validation as a process. To elucidate this, they provide a guiding framework and present a case illustration that will assist an interpretivist entrepreneurship researcher to establish and demonstrate the quality of their work.
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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 | 0.160 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.088 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".