Bibliographic record
Abstract
The focus of many Universities or Colleges is split between its customers, the students, and the regulators who make sure the large-scale requirements of the University are being met. The institution is required to provide more services to more students, but not with accompanying increases in resources. This tends to cause rifts within the Institution as some needs are met, but others are not. The Information Systems of these institutions are prone to rifts as well, and those discrepancies show as information gaps. If vital information is either not captured, or is required to be captured repeatedly and independently, then these gaps cause inefficiencies and frustration of all information stakeholders. To bridge these gaps at the grass root level, departments and faculties at post secondary educational institutions attempt to implement their own proprietary information systems. These systems are designed to provide units with up to date, integrated, local information and are used for both internal and public interfaces. In this case, however, institutions are left scattered with multiple non standard, single use tools which do not scale. At the same time, the top down approach of many universities and colleges is to implement one of the comprehensive administrative products from industry leaders such as Oracle, IBM, SAP or others. But as we argue in this paper, these enterprise systems alone do not fulfill all of the information needs. With their arrival, there is an even bigger need to consolidate software which shadows these systems, and to connect all the pieces of software puzzle together. Our institution recently implemented Oracle's PeopleSoft Enterprise and requirements and expectations have dramatically changed as a result. The PeopleSoft Enterprise product is a comprehensive and advanced tool to manage large organizations, but academic units of the institution need to manage more and different scope of data than PeopleSoft exposes. This paper argues that our Unit Information System (UNITIS) fills both horizontal (inter faculty and departmental) and vertical (administration versus faculties) information gaps for academic units and integrates well with enterprise solution our institution choose to implement. UNITIS is an information system designed by and for academic units to manage their daily operations. But its main strength is in its ability to tie academic units into the institutional enterprise system.
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".