Building a workplace of choice: Using the work environment to attract and retain top talent
Bibliographic record
Abstract
Today, organisations around the globe are operating in an unprecedented, highly competitive seller’s market. The global workforce is now more mobile than ever before, meaning that companies are no longer simply competing for talent nationally, but rather on an international level. The Canadian Federal Government, like most Government organisations, simply cannot compete with private industry in the area of salaries, stock options or perks. In addition, the impending wave of retirements that threatens to devastate the Federal employment ranks has caused us to look to the work environment as a means of attracting and retaining the top talent we need. This paper examines the characteristics of the different generations that currently make up our workforce and discusses what they, as well as new recruits, expect from their employers and from their work environments. It also delves into the role the workplace plays in recruitment and retention and the way in which it can be used to improve an organisation’s corporate identity. It then looks at what types of perks are actually valued most by employees, and explores how the physical environment can be aligned to help shape a company’s organisational culture and facilitate the communication, teamwork and creativity that are necessary to sustain a culture of continual innovation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".