Bibliographic record
Abstract
Supporting business Ph.D. students requires thinking about these students foremost as junior researchers, so there is the need to bring into focus specific library services that support the research lifecycle.Northwestern University Library uses multiple ways to get the word out to these students about how the library can be a valuable resource for any stage in the research process.Data management, copyright consultation, digital publishing, general research assistance, and workshops specifically geared towards graduate students on a myriad of topics are the core services we offer.For example, with more funding agencies requiring data management plans, students need to have a framework for understanding what all this entails.Data management consultations offered by experts in the library offer assistance for drafting data management plans and understanding funding agency requirements.As interest in data management continues to grow, there are many organizations that provide insights and tools, such as the DMP Tool, which bills itself as a free, open-source, online application that helps researchers build data management plans.The library vets and curates these resources, which include metadata guide sources and data repositories, including Arch, the Northwestern University data repository.Arch data repository offers faculty and students the opportunity to preserve and offer access to their data free of charge as long as it is digital, unrestricted, and in support of publication.Related to Arch is the digital publishing initiative the Northwestern University Library has built over the past few years.Not confined specifically to Ph.D. students, it offers all Northwestern affiliates a chance to publish original research online.Open access monographs, conference proceedings, and open licensed course materials are a few of the materials the library's digital publishing group can assist with.https://doi.org/10.3998/ticker.3468©2022 Carol Doyle data analytics.Finally, information on connecting to experts for data management, copyright, digital publishing, and advice on scholarly communication rounds out the LibGuide.Every fall, the library features a Research Resources Forum geared specifically to Ph.D. students and offers presentations on information sources by academic discipline, GIS, special collections, and more.A Connect with Your Librarian session is a feature of this event, and we use this as an opportunity to meet with business Ph.D. students to discuss research interests and library services.Also, our business school publishes a newsletter for Ph.D. students, and it features library services.In summary, while an online research guide, such as a LibGuide, is a useful way to bring into focus the resources and services that Ph.D. students need, we look for every opportunity to get the word out through workshops, presentations, and media throughout the year.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 teacher head, 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".