Bibliographic record
Abstract
Abstract The author is a globally recognized leadership and corporate culture strategist. He describes important demographic shifts in his home country of Canada, and worldwide, with implications for leaders and managers now and in the future. Life expectancy is rising and people are contributing at much later ages. He cites the example of Leader to Leader Founder Frances Hesselbein, who lived and worked until the age of 107. People are working longer and birthrates are plummeting; as cited in the article’s statistics. He proposes a “new workforce age model: Rivers, Rocks, and Rubies.” Each is described in detail, but briefly Rivers are “early‐career contributors,” Rocks are “mid‐career stewards,” and Rubies are “seasoned contributors, those who carry hard‐earned wisdom, panoramic insight, and long‐view decision‐making.” Rather than a traditional and linear career ladder, he describes moves within a “career canvas,” which in his words are: stay put, move up, move down, move lateral, move out, spark up, slow down, phased out, and boom in. As he notes, a “team member’s role can shift in nine different ways, depending not only on their era, but also on their life stage, career aspirations, and performance profile.”
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".