Bibliographic record
Abstract
Chris Milan, Managing Director of Southeastern Region at Tribridge, Inc., was drumming away at the annual “Connect” conference with the company band called “The Bridge.” He enjoyed seeing everyone dancing, laughing, and jamming out to the music and making new friends with coworkers. Tribridge had quickly grown over fifteen years to more than 600 employees with most deployed to customer sites around the U.S. and Canada. The annual conference was a cultural staple designed to re-connect the company with employees and employees with each other. But how much longer could they continue to rely on a once a year event to keep the company together on both social and cultural levels? Chris reflected on a recent executive team meeting where the leaders asked themselves, “How can we keep all of these people, from all over the globe, feeling connected with each other?” The leadership was familiar with and had been discussing ways to keep the company connected through the deployment of an Enterprise Social Network (ESN)--sort of a Facebook for employees. They had been told that an ESN would allow for local employees and remote employees to connect more efficiently to help create an overall cohesive work environment. In theory, it would be a much less expensive approach than flying everyone in to Tampa. And, it was supposed to create a continuous--not just once a year--flow of interactions through an online environment. Plus, wasn’t everyone already familiar with the tool? After all, nearly everyone was on Facebook. Why not set up an ESN and they could join that too? At the same time, the decision to proceed wasn’t easy. There were many factors Milan and the leadership had to consider. Email, Instant Messaging (IM), phone calls and SharePoint were Tribridge’s current forms of communication and connectivity. Would connecting through an ESN replace those platforms? Would it be “in addition to” them? Also, Tribridge was a “Microsoft shop” using Office 365. Office 365 included the ESN platform called Yammer. Would using Yammer be more efficient than email for communication? Would it be as effective as a party for connectivity? Could it share and propagate a culture with a distributed workforce? Since Yammer seemed to be the inevitable choice at Tribridge, maybe the real questions would revolve around how to implement another system in the already busy world that was Tribridge.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.611 | 0.331 |
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".