Bibliographic record
Abstract
The HR Lab served as an incubator within Bosch that rejuvenated the company with a history of more than a hundred years. Agile transformation solutions debuted in the Power Tools (PT) business unit, driven by the industry's evolutionary needs. Bosch used to sell Power Tools to two separate groups of consumers: Professional tools for users in trade and industry, and DIY, accessories and garden tools to amateur crafters like families or avid gardeners. However, e-commerce brought disruption to the past business model and threatened the unit's competitive advantage. To proactively provide employees with innovative solutions geared towards consumer needs, Rosa Lee, Executive Vice President of Bosch China, came up with the idea of the HR Lab. The idea followed rounds of discussions with Uwe Raschke, Bosch's board member responsible for Consumer Goods (including the Power Tools division and BSH Hausger?te GmbH), and Elly Siegert, Bosch’s Vice President for Human Resources in the Power Tools BU. HR Lab products such as Individual Development Dialogue, Team Staffing, Peer Recognition and Development Navigator were implemented in various Power Tools offices worldwide and received very positive feedback. However, new emerging issues pushed Lee to reflect and rethink how to measure the tools’ efficacy and collect valid feedback for the initiative. After years of launching so many new HR Lab products, what should the team do next to achieve agile transformation for the entire company? Should Lee keep running the HR Lab or press pause to consider the future first?
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.065 | 0.036 |
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".