Technology: Democratizing Access or Exacerbating Inequality
Bibliographic record
Abstract
The goal of this symposium is to bring together scholars studying the distributional effects of technology to address three questions: (1) For whom can technology democratize access? (2) Can technology exacerbate inequalities? (3) What can managers and policymakers do to facilitate the equitable distribution of technology-enabled opportunities? To this purpose, the symposium consists of four unique papers that study questions around equity and equality related to the diffusion and adoption of various technologies (i.e., sound synchronization technology in movies, mobile money, AI, and crowdfunding platforms). With diverse theoretical perspectives (i.e., organizational technology adoption, industry emergence, labor employment, and social exchange), different levels of analysis (i.e., individual, organizational, market, and country levels) and various methods (i.e., historical and archival, abductive, survey experiments, and matching in large samples), these four studies together represent a thoughtful inquiry into the issue of technology and inequality and shed light on when and under what conditions technology may be more or less likely to foster (in)equity. Beyond the Decibels: U.S. Movie Theaters’ Adoption of Sound Synchronization Technology, 1927-1931 Author: Ying Li; Hong Kong U. of Science and Technology Author: Laura E. Dupin; Amsterdam Business School, U. of Amsterdam Regulatory Uncertainty, State Fragility, and Emergence of the African Mobile Money Industry Author: Audra Wormald; Kenan-Flagler Business School, U. of North Carolina at Chapel Hill Who Needs College? Employers Value Adults with Training Certificates Author: Hatim A. Rahman; Northwestern Kellogg School of Management Author: Nicole Kreisberg; - Does Equity Crowdfunding Provide Better Funding Opportunities for Underrepresented Founders? Author: Melody Chang; USC Marshall School of Business
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".