Bibliographic record
Abstract
“If everyone is moving forward together,” said the American industrialist Henry Ford, “then success takes care of itself.” The unspoken challenge inherent in that statement is figuring out how: How does a business achieve such synchronized effort? For the managers of Abbott Diagnostics Longford, a healthcare manufacturing facility in Ireland, the answer lay in deploying lean Six Sigma strategies for continuous improvement and linking core competencies to the Shingo principles of operational excellence. As Seán Kelly explains on page 6, the resulting boost in employee empowerment and refinement of day-to-day processes helped the site save more than $22 million in eight years and capture international acclaim. Performance-based coaching is another valuable tool to promote productivity and employee development, note Tim Toterhi and Ronald J. Recardo. On page 25, they detail a comprehensive coaching model that is designed to deliver a quantifiable return on investment and can be tailored to any business environment. When organizational leaders seek progress through mergers and acquisitions, they can learn much by first considering the experiences of others who have done the same. On page 41, Nitin Pangarkar outlines the policies that Nestlé-Alcon, Philip Morris-Miller Beer, and Mittal Steel have used to shore up value for both sides in a corporate acquisition. Meanwhile, the experiences of Pfizer, which Syed Tariq Anwar reviews on page 56, caution organizational leaders to never underestimate the impact that external stakeholders can have on their plans. When looking for creative ways to motivate staff, it is crucial to bear in mind that even within the same field, no two types of employees are alike, notes Saïd Echchakoui on page 70. His study of the effects of personality traits and organizational identification on the turnover intention of agents in a Canadian call center illustrates the importance of examining employees’ total work experience when devising policies to encourage their growth and commitment to the firm. And as Xiaodong Yang explains on page 87, the characteristics of emotional intelligence that affect personal relationships can also determine the effectiveness of individuals and teams in a corporate setting. He presents a useful model to uncover strengths and weaknesses in organizations of all sizes and cultures. Clearly, when devising a strategy for moving an organization ahead, there is no single answer; but a positive outlook can help. As Ford also advised, the key is to focus not on fault finding, but on finding a remedy.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.203 | 0.144 |
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".