Bibliographic record
Abstract
following hypothetical human resources issue has, like so many of its ilk, no correct solution. To help your colleagues deal with such a situation, please tell us how you would resolve it. We'll print as many as space permits. Editor entered the XYZ R&D Laboratories early Monday morning, thoroughly recharged from two weeks fishing in the woods of Northern Canada. He felt ready to take on the world and certainly any problems that XYZ might throw at him. A senior staff scientist at XYZ, Al reported directly to the vice president of research, Evers, and was considered a world-class expert in plastics processing. He carried the responsibility for a considerable research program and, in addition, was highly regarded by XYZ's manufacturing divisions for his ability to solve plastics processing problems. Al's love of the outdoors was well known and that, together with his considerable size, had gained him the nickname, The Bear. divisions were quick to contact him when something didn't go right with a manufacturing process-the slogan in the plastics manufacturing areas being Send for whenever a serious problem arose. first thing Al did upon returning to his laboratory office was to check his voice-mails. With two weeks away and no contact, he anticipated a long list. have 58 messages. Message marked urgent. Al, this is Tom [the VP]. We've been trying to track you down. Major production issues at Lima. See me immediately upon your return. If not sooner! Have You Been? Al went to Tom's office and found him sullen and angry. Where have you been? was Tom's greeting. was on vacation for the last two weeks-the first real one I've had in three years, Al replied. told you when I left that I was going on vacation. knew you were on vacation, Al, but remember, I've instituted a policy with all senior people who report to me, and particularly those of you who have strong relations with the manufacturing divisions, that I expect to be able to contact you 24 hours a day, seven days a week if necessary. With a cell phone and a laptop computer-both of which the company provides you-I don't see any real problem. In this case, the Lima division had to shut down a production line because of a severe quality problem with the output of some of their injection molding machines. They asked for Bear immediately and desperately wanted your help. I tried calling you on your cell phone-no answer-and my e-mails produced no response either. Al responded, Tom, I was aware of your policy, but in the woods I was truly out of reach and there was no way I could have adhered to your policy. I really needed the vacation-my research plus helping with manufacturing has kept me under considerable stress. 24-hour Engineer Tom responded, Get on the phone immediately with the Lima Division people and see what you can do-you've already cost them thousands of dollars because of the delay. And don't feel I'm picking on you-I know of other companies that have similar rules for key people. That's the reason we provide you with cell phones, pagers and laptops. And I just read in Penn Stater (Nov/Dec 2002) that some universities offering online courses are drastically increasing the schedules of their faculty-one professor refers to himself as a '24-hour professor'! Al contacted the appropriate people at Lima and learned they were still running far below capacity because of bad-quality parts being produced by a few of the division's injection molding machines. Before thinking about visiting the division, Al requested data related to the raw materials being used and some measurements from the bad parts being produced be e-mailed to him for study. He also asked a number of questions and suggested these answers and the requested data be sent to him post-haste. Three days went by with Al receiving nothing from the Lima Division. …
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.859 | 0.823 |
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".