Glassdoor's best places to work internationally: Are they best for shareholders?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In 2015, Glassdoor published its first international Best Places to Work list in the United Kingdom. Since then, Glassdoor has begun publishing 10 different Best Places to Work lists in 9 different countries. Glassdoor's Best Places to Work lists are unique in that rankings are solely based upon employee reviews and are not influenced by self‐nominations or a cost paid by a company. With 64 million unique visitors each month, these Glassdoor lists have the potential to impact investors. In this paper, we explore whether firms appearing on lists for Canada, France, Germany and the United Kingdom result in short‐term announcement effects or long‐term abnormal returns on a raw‐ and risk‐adjusted basis. We find that the Canadian sample earns statistically significant abnormal returns in the announcement window 5 days after the announcement date. In the long run, we find that the Canadian sample also outperforms its matched sample and local index on a risk‐based basis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it