Bibliographic record
Abstract
Contents: Fausto Colombo/Leopoldina Fortunati: Introduction. Broadband, Media and Generational Approach: a New Starting Point? - Fausto Colombo: The Long Wave of Generations - Michael Corsten: Media as the Historical New for Young Generations - Piermarco Aroldi: Generational Belonging Between Media Audiences and ICT Users - Jukka Kortti: The Problem of Generation and Media History - Marriann Hardey: ICTs and Generations - Constantly Connected Social Lives - Giovanni Boccia Artieri: Generational We Sense in the Networked Space. User Generated Representation of the Youngest Generation - Andra Siibak: Online Peer Culture and Interpretive Reproduction on the Social Networking Site Profiles of the Tweens - Mutlu Binark/Gunseli Bayraktutan Sutcu: Usage Patterns of New Media by Turkish New Middle Class Young People - Ariela Mortara: Generations and Media Fruition of Social Networks - Marco Centorrino: The Image of the and the Generation Gap - Matteo Treleani: The Access to Memory in Video Archives On-Line. Generational Roles on YouTube and Ina.fr - Agnese Vellar: Lost (And Found) in Transculturation. The Italian Networked Collectivism of US TV Series and Fansubbing Performances - Leopoldina Fortunati: Digital Native Generations and the New Media - Vesna Dolnicar/Sonja Muller/Marco Santi: Designing Technologies for Older People: a User-Driven Research Approach for the SOPRANO Project - Alberta Contarello/Mauro Sarrica/Diego Romaioli: Ageing in a Broadband Society. An Exploration on ICTs, Emotional Experience and Social Well-being within a Social Representation Perspective - Eugene Loos: Generational Use of New Media and the (Ir)revelance of Age - Chiara Carini/Ivana Pais: Business Social Networks: an 'Age Levelling' Service? - Tanja Oblak Crnic: The Generational Gap and Diverse Roles of Computer Technology: The Case of Slovenian Households.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".