To Share or Not to Share: Exploring the depths of digital identity through an entrepreneurial lens
Bibliographic record
Abstract
As founders build and grow their ventures, most establish an entrepreneur identity (Burke, 2016). Today, most founders create and make extensive use of a venture-specific website to support their firms. In advance of this study, we observed that some founders present a well-developed digital identity on their websites, while other founders do not. Existing identity research both online and offline offers little guidance to help explain this observed difference. Moreover, current research on online identity construction is highly fragmented (Huang et al., 2021). This study’s focus is on answering the research question: What accounts for the observed differences in founders’ digital identities online? We undertake to answer this question adopting a qualitative method, conducting 34 depth interviews with founders across Canada. This study’s findings will inform identity control theory (Marcia, 1993), and will help entrepreneur support organizations to better prepare entrepreneurs in managing their digital identities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".