Meeting the Challenge of Rapid Change in Media Industries: A Case Study in Media Programs at Canadian Colleges, Polytechnics, and Universities
Bibliographic record
Abstract
The purpose of this study was to investigate the extent to which keeping pace with rapid change in media technologies and production techniques poses a leadership challenge for post-secondary institutions offering media production programs. Contributing factors to the challenge include declining funding for capital investment, and lengthy review and approval processes for curriculum development and revision. These factors, as well as educators’ best practices in addressing the challenge, were considered in the context of relevant literature on media industries, change management, and leadership practice. Utilising a particularistic case study methodology and explanatory sequential data collection methods, the study sought to investigate a practical problem arising from everyday practice through collection of a diverse set of both quantitative and qualitative data. Specifically, data were collected through questionnaires from 96 participants working in post-secondary institutions and was supplemented by questionnaires completed by 25 media industries employers. A subset of 20 respondents to the post-secondary questionnaire participated in follow-up interviews to further clarify the findings from both the post-secondary and industry questionnaires. It is apparent from the findings that rapid and increasing change in media industries poses a very real challenge for media educators. The currency of curriculum and technical resources is an important factor in ensuring students graduate with the skills and abilities necessary to enter the work force. While there is no single ready solution to this problem, the findings also revealed the multiple tactics media educators have developed to mitigate the impact of rapid change, and the respondents’ perspectives on the potential value that project management tools, changes to organisational culture, and leadership styles might have for improving outcomes in this area. It is hoped that these findings will prove useful to media educators tasked with deciding on technologies in which to invest and at what time, and how best to integrate new production techniques into curriculum. It is also hoped that these findings will prompt further study, expand the conversation to additional stakeholders, and contribute to larger conversations around academic program development and delivery.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.051 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".