Customers as trade secrets v. employees as market makers: introducing the unified approach of innovation policy
Bibliographic record
Abstract
This thesis presents a coherent narrative of the laws of trade secrets and confidential information in the employment context. In the process, it asks about the best policy to adopt when evaluating the enforceability of trade secrets and restrictive post-employment covenants in the employment context, especially with regard to customer connections and related information. This thesis also examines what governments should do in order to engender inventiveness in general, and innovative enterprises in particular. A comparative analysis, primarily of the law of Canada and Israel, is employed and jurisprudential and economic arguments are invoked. It is then suggested that there is a relationship between incentives to create and the extent of the enforcement of the laws of trade secrets and confidential information. This thesis therefore concludes by proposing innovation policy as a concept which encourages creativity and unifies the disparate concerns of intellectual property, employment law and the parties involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.015 | 0.009 |
| 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".