From diversity to cross-culturalism: the evolution of human resource training within the canadian broadcasting corporation
Bibliographic record
Abstract
The objective of this master's thesis is to investigate the transformation of human resource management regarding a particular area of professional training and development. Specifically, the evolution of diversity training to incorporate facets of culture, like heritage, language and cross-cultural interactions, will be detailed an analyzed. \n \nWithin the literature review, this will consist of a deductive examination of the past, present and future of three organizational elements: human resource management, diversity training and cross-cultural training. Through the lens of fluctuations and advancements in globalization, internationalization and immigration, shifts in theoretical premises and actual practices will be discussed. \n \nThis will then be coupled with a history of public service broadcasting and, eventually public service media, as well as their relevant tenets and techniques. The following section will present a broad history of the case, the Canadian Broadcasting Corporation. It will also contain relevant information on the current market position, internal mechanisms and external efforts of the CBC. \n \nThe overall method of case study was implemented with open-ended interviewing via a semi-structured questionnaire previously utilized in a similar study by Lorraine Gutierrez, Jean Kruzich, Teresa Jones and Nora Coronado in their 2008 article Identifying Goals and Outcome Measures for Diversity Training, found in the journal Administration in Social Work. The purposive sample (N=7) was interviewed at the CBC headquarters in Toronto, Ontario, Canada in February 2014. The major findings of the study were portrayed in a timeline format spanning 6 generations (1970s/1980s, 1990s, 2000s, 2010 to present [2014] and future) because each era strongly reflects patterns found in the literature embodying the theme of diversity and cross-cultural training and management. \n \nFinally, the concluding chapter will introduce implications, caveats and ideas for future research. Most importantly, it is the ambition of the entire document that these implications will generate insights regarding the entangled nature of internal and external elements of diversity and culture within organizations; the future of human resource training as more organic and casual; the expansion of internationalization beyond surface-level topic selection; and the fluid nature of diversity in an ever-changing media landscape.
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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 | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".