Bibliographic record
Abstract
This Trends Package has been developed by Suzanne Stein, Super Ordinary Lab, OCAD University, and Scott Smith, Changeist. Draft trends from which the package was developed were collected via two Trend Workshops held 29 June and 6 July 2010, at Strategic Innovation Lab (sLab), OCAD University, Toronto. Workshop contributors included participants from the project partnership and from the Cluster. This document is a product of our ‘horizon scanning’ process. Trends and Countertrends represent directional patterns in data, a rising tide of signals, in which, for example, a critical mass of headlines about people using Facebook to call for help in emergency situations points to a larger trend regarding the increasing mission-critical importance of social networks. To date we have identified more than sixty trends at the project website. 2020 Media Futures is a multi-industry strategic foresight project designed to understand and envision what media may look like in the year 2020; what kind of cross-platform Internet environment may shape our media and entertainment in the coming decade; and how Ontario firms take action today toward capturing and maintaining positions of national and international leadership. The project asks: In the face of sweeping and disruptive changes driven by the Internet, how can we help companies in the book, film, interactive, magazine, music and television industries — Ontario’s Creative and Entertainment Cluster — to better identify emerging opportunities, create more resilient strategic plans and partnerships, boost innovation, and compete in increasingly demanding global markets?
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.362 | 0.205 |
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".