Bibliographic record
Abstract
Yoshi Takei faced a major career challenge as he headed towards a meeting with his team. His team experienced a significantly higher rate of layoffs than comparable teams throughout the organization. Takei was a Japanese-American manager of a team of network engineers that worked in a Fortune 100 telecommunications company, Everlast, based in the Northeast region of the U. S. Promoted to a management position after excelling technically as a software engineer, he and his fast-paced engineering team provided critical network builds for major government and business clients. Takei knew his team was overworked, overstressed, and beaten down, but he had more bad news to deliver. Downward pressure forced him to cut one more person. His employees worked more than 60 hours per week already and eliminating another employee was likely to seriously damage whatever morale was left in his group. The economy was in the doldrums. Everlast, driven by quarterly earnings, had experienced a number of reductions in force (RIFs) since 2008. His challenge after the series of layoffs was how to rebuild trust and credibility with his team as part of his efforts to rejuvenate his flagging managerial career at Everlast. Mr. Takei's Team and the RIF Process for Networking Engineers Takei once oversaw a team of 12 employees, but due to quarter-over-quarter downsizing over the past year-and-a-half, his team now consisted of just seven engineers. Other managers who ran the same function in other states made it through most of the corporate force reductions without losing a single employee; whereas, Takei administered the busiest territory, but was forced to make painful cuts every quarter. Gamesmanship and politicking tended to play a significant role in the reduction in force (RIF) process at Everlast, or so Takei believed. These activities were abhorrent to Takei who believed that engaging in these activities were beneath him. His team had a huge network build to complete within the next eight weeks for a major customer, so he needed everybody focused on the network build because the regional executive management team had placed the project under close scrutiny. On the surface, Takei knew that RIFs at Everlast were determined by assessing candidates for downsizing according to the following criteria: area of company where employee worked, productivity, teamwork, quality, customer satisfaction, and attendance. From Takei's experience as engineering team manager, he believed the ultimate criteria for retention of employees ultimately depended on the grandstanding and political behavior of their manager at RIF meetings. Bob Bazile, Takei's direct supervisor, served as head of the Regional Executive Committee and was also head of the Networking Engineer RIF committee. Takei was also aware that Bazile had told him that Takei was not his first choice to head up the network engineering team Takei now led. Bazile's first choice was Joe Archilazzi, a personal friend and golfing buddy of Bazile. While a decent engineer with a knack for developing contacts and networks both inside and outside Everlast, Archilazzi had a reputation for cutting corners in order to uphold the deals he made. Bazile's choice of Archilazzi was overruled by the Everlast Corporate Management Development Board that had responsibility for succession planning and was the ultimate arbiter of appointments at this level. …
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.052 | 0.018 |
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".