Bibliographic record
Abstract
Abstract Using state-of-the-art data-mining techniques, this research constructs a unique dataset comprising the votes cast by Chinese shareholders on 15,553 resolutions laid before 2,888 general meetings of Shanghai Stock Exchange-listed companies in the six-year period between 2015 and 2020. This research empirically depicts, for the first time, the manner in which minority shareholders exercise their rights to vote in China, presenting a counter-thesis to the conventional wisdom of shareholder passivity. In particular, this research offers a unique perspective on how the COVID-19 pandemic has influenced Chinese shareholders’ voting behaviour by focusing its empirical investigation on the 76-day time window from 23 January to 7 April 2020, during which period Wuhan, the epicentre of the COVID-19 outbreak in China, came under a mandatory lockdown order. Our findings offer strong empirical evidence that Chinese shareholders cast their votes in a characteristically more informed manner in the 2020 sample period than they did during the sample periods in the previous five years. The research highlights the potential of shareholder activism in economies where share ownership is concentrated. It also has implications for the ongoing discourse on virtual shareholder meetings and the shareholder franchise.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".