The Unsung Heroes of Training and Development in Canada, The Administrators: A Content Analysis of Job Announcements
Bibliographic record
Abstract
Research that explores competencies needed by Training Administrators is limited; yet the role of Training Administrator is common. The purpose of this study was to define the role of Training Administrator from the industry’s perspective of Training (Learning) and Development with respect to its main roles and responsibilities, soft skills, education, and technical requirements needed to perform the job successfully, and what the typical title of the job is. To determine definitions, 63 job announcements from across Canada were collected from one online job database (LinkedIn.com) over a five-month period in 2021. Following a systematic process of collection, coding, and the measurement of frequency, by which a role and responsibility category, as well as a stated superior-level soft skill, was found within each job announcement, five main role and responsibilities and eight superior-level soft skills emerged. Moreover, the required minimum education, experience, and technical skills were identified from an employer’s perspective. The results suggested that those in the role of Training Administrator were mainly expected to perform the roles and responsibilities of: \n1.\tLearning Management System (“LMS”) Administrator. \n2.\tLogistical Support. \n3.\tData Analytics. \n4.\tDesign and Development, of curricula. \n5.\tLearning Communication Specialist. \nThe eight soft skills expected at a superior-level skillset were found to be in: \n1.\tOral and written. \n2.\tInterpersonal. \n3.\tMulti-tasking. \n4.\tDetail-oriented. \n5.\tTime management. \n6.\tAdaptability. \n7.\tStakeholder management. \n8.\tSelf-motivation. \nA typical job title for the role as determined by the current study was Learning Coordinator, rather than Training Administrator.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".