“Mapping the Island”: Data Journalism Education through Coverage of Gentrification and Housing Issues in Montreal
Bibliographic record
Abstract
The focus of this research-creation project is how data journalism can be learned and applied to the coverage of housing issues in the city of Montreal. In examining this subject, several questions are raised about the nature of data journalism as a subsection of the larger journalistic field. This research aims to investigate how data journalism practices can be learned, taught and incorporated into the existing work of journalism studies, through coverage of the city, housing and gentrification. \n \nIn applying a “learning-by-doing” model of research-creation, conducting this project did lead to higher competency in data journalism practices. The results of this experience indicate ways data journalism education could be applied differently in university and mid-career journalism training curricula, namely, using a specialized data journalism project to develop a specific set of data journalism skills and making those skills more accessible by focusing on a localized story. It was also determined that in creating a data journalism project that focused on a specific story—in this case, housing issues in Montreal—there was a need to narrow in on a particular set of skills—namely data analysis, programming and mapping proficiency—similar to how traditional reporters must develop certain skills to cover different beats in a newsroom. This research indicates that a more widespread application of this learning-by-doing model in data journalism training and education has the potential to allow students to more deeply develop specific skill sets that would lend themselves more effectively to the expansive and collaborative practice of data journalism in the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".