Bibliographic record
Abstract
Media concentration is more important than ever in an age of mobile phones, the internet, and information abundance. Taking Canada as an example, this chapter investigates how telecommunications, internet and media industries are becoming more concentrated, and whether the fear of domination by internet giants like Google and Facebook is justified. Introduction This chapter offers a guide on how to study the media industries in the age of the internet and mobile phones, using Canada as a case study. It is based on research done as part of the Canadian Media Concentration Research (CMCR) Project and reflections on how to recast how our field thinks about the political economies of communication. It draws on lessons learned from work done as part of the International Media Concentration Research Project, a project spearheaded by Eli Noam that resulted in the publication of Who Owns the World's Media (2017) – an authoritative and detailed review of the telecommunications, internet, and media industries in thirty countries. It also relies on experience gained from participating in several contentious policy and regulatory proceedings that have shaped the internet, mobile wireless, and media in Canada in recent years. The starting premise of this chapter is that we must take the media industries as serious objects of analysis, and clearly define what we mean by ‘the media’. Media concentration is more important than ever in an age of mobile phones, the internet, and information abundance. This chapter will introduce some of the essential sources, tools, and challenges that are present in this sort of research. The aim is to encourage engaged, independent, and critical scholarship that is reliable, reasonably easy to use, and open to others to verify and use for their own research. Like everybody, media researchers have limited time, resources, and knowledge and, consequently, they must set a hierarchy of research priorities. This means putting the structure, dynamics, economics, evolution, and forces that shape the media industries at the top of the list (Garnham, 1990). This focus is crucial because we live at a critical juncture in time when decisions made in the near future will shape the media landscape for decades – if lessons from the ‘industrial media’ set down in the nineteenth and twentieth centuries are any guide.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".