Glassdoor's best places to work internationally: Are they best for shareholders?
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".