Bibliographic record
Abstract
We do business in a results-oriented world. Our focus on growth is laudable for its clarity, but one of its downsides is that firms can lose sight of the process: how business gets done and the individuals or employees through whom results are achieved. This leads to compromised decisions and unethical behavior. It is not just what we accomplish that matters but also how we accomplish it. In The Process Matters, Joel Brockner shows that managers have to do more than just meet targets and goals. They have to reach those ends in the right ways—with input, consistency, and accountability—if they want to effectively lead and manage in their organizations. Brockner discusses what goes into the right process, how it leads to better outcomes, why it is easier said than done, and how to overcome obstacles along the way. Brockner demonstrates that a high-quality process often costs little and may not even require a great deal of time. In light of these facts, he considers the puzzling question of why good business practice doesn't happen more often. Brockner draws from various real-life workplace examples—from Jay Leno's departure (twice) from his TV show, to the improvement of shooting accuracy in the U.S. Navy, to the surprising results of layoffs in Canada. He also factors in a wide swath of studies to examine such issues as the importance of perceived fairness in the process, the management of organizational change, and the encouragement of a strong sense of self in those involved in decisions—in short, the ways that managers can bring out the best in their people. Relevant to anyone who is in a managerial position—from the CEO on down—The Process Matters proves that seemingly simple differences in process can go a long way.
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 | 0.032 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.052 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".