Bibliographic record
Abstract
As developed countries shift more toward knowledge-based economic activities, information, technology, and learning play an increasingly important role. The use and adoption of new technologies by firms and workers constitutes a critical component of the process of technological diffusion and advancement. A paper by CLSRN affiliates Craig Riddell (University of British Columbia) and Xueda Song (York University) entitled “The Role of Education in Technology Use and Adoption: Evidence from the Canadian Workplace and Employee Survey†(CLSRN Working Paper no. 83) investigates the causal effects of workers’ educational attainment on their use and adoption of new technologies. The study shows that education exerts causal impacts on certain measures of technology use and adoption, although not all. Since its launch in 1995, Craigslist, has served as a platform for users to post ads that centre primarily on jobs, housing, services, personals, or for sale items, and has grown to receive more than 20 billion page views per month – making it one of the most visited websites in the world. The advent of the internet age has revolutionized the way people search for goods, services, housing and even friends. The job-search market and apartment and housing rental market have been virtually transformed since the emergence of Craigslist and the wide abundance of easily accessible information has affected the way these markets function. A study by CLSRN affiliates Kory Kroft (University of Toronto) and Devin Pope (University of Chicago) entitled: “Does Online Search Crowd out Traditional Search and Improve Matching Efficiency? Evidence from Craigslist†(CLSRN Working Paper no. 108), finds that Craigslist significantly lowered classified job advertisements in newspapers, caused a significant reduction in apartment and housing rental vacancies, but had no effect on unemployment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.475 | 0.344 |
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".