Innovation Management in Canadian Newspaper Newsrooms: Identifying Blocks and Enablers to Facilitate Digital Change
Bibliographic record
Abstract
Local newspapers have become fixtures in communities as archivists, watchdogs over people with power, and as trusted sources for information and analysis.This thesis examines the challenges newspapers face as they reinvent themselves as digital media companies.When a legacy company attempts to innovate, it will encounter a number of predictable forces that will stand between it and change.Innovation blocks are associated with newsroom culture, processes, and physical assets that have become so engrained over time that they inhibit new ways of doing things.By identifying what blocks exist, specific strategies can be developed to overcome them.This theory was applied to three Canadian newspapers that had just gone through significant innovation projects.The research provides insight into what factors blocked each of these projects along with specific strategies that were used to enable change.iii Acknowledgements For many of us working in legacy news organizations it was exciting when leaders first shared their "digital first" strategies.It meant that we would finally start meeting our potential as digital content producers.But change was slow --and I wanted to understand why.I returned to Carleton University in the fall of 2014 to complete my master's degree and attempt to answer this question.The following report is the result of that effort.This thesis benefited from the assistance of two esteemed advisors.I owe an enormous amount of gratitude to Professor Aneurin Bosley who was instrumental in crafting the research design and helping to set up case study research at both the Ottawa Citizen and Toronto Star.An equal amount of gratitude is owed to Professor Chris Waddell who stepped in as advisor for the final four months of the project.Professor Waddell provided consistently thoughtful feedback and revisions on my chapters, often within hours of receiving them.Professor Waddell's assistance in putting this project together in its final few weeks was invaluable.Professor Klaus Pohle deserves special mention for having been an un-official advisor.Professor Pohle indulged me in many conversations about my topic and had many helpful tips along the way.This project would not have been possible without the co--operation extended by the Ottawa Citizen, Toronto Star and La Presse.It was an incredible privilege to have access to these newsrooms during such an important period in their organizational history.Interview participants were generous with their time Conclusion and Key Take--Aways .............
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".