The Quest for Management Research on Big Data and Analytics
Bibliographic record
Abstract
Although big data and analytics have been around for more than two decades and are extensively used by large multinational corporations, management research on the topic has been slow to react. Hence, this symposium intends to shed light on the role that management and organization scholars could play in helping organizations to effectively use big data and analytics for process improvement. It includes three papers that analyze the use of big data and analytics in decision making, crowdsourcing, and human resources management. A Dynamic Process Model of Big Data Analytics in HRM Presenter: Minghui Cheng; McMaster U. Presenter: Rick D. Hackett; McMaster U. Crowd IQ: Optimizing the quality of crowdsourced solutions using data analytics Presenter: Michal Kosinski; Stanford U. Cognitive Analytics: Making Sense of Data to Improve Decision-Making Presenter: Constant D. Beugre; Delaware State U.
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 | 0.064 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.036 | 0.084 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".