Amplify Your Impact: An Interview with Mark Aaron Polger, Editor of <em>Marketing Libraries Journal</em>
Bibliographic record
Abstract
Mark Aaron Polger is the First Year Outreach Librarian at the College of Staten Island, City University of New York (CUNY), where his responsibilities include promoting library services and resources as well as providing instruction to first year students. Polger is also an Information Literacy Instructor at ASA College. His research interests include library marketing, outreach, and user experience design. He is active in LLAMA as the chair of the PR XChange Committee as well as the co-chair of the Annual PR XChange Awards Competition. Regionally, he is an active executive board member of ACRL/NY (Association of College and Research Libraries, Greater Metropolitan New York Area), where he serves on the planning committee of the annual symposium and co-chairs the User Experience Discussion Group. Locally, he co-chairs meetings in New York City for ACRL National’s Library Marketing and Outreach Interest Group. He is also a member of the planning committee of the annual Library Marketing and Communications Conference (LMCC). He is co-chair of the LACUNY (Library Association of the CUNY) Library Marketing and Outreach Roundtable Discussion Group.Currently, Polger is the founder and editor-in-chief of the new open-access, peer-reviewed Marketing Libraries Journal, which was launched in fall 2017.Originally from Montreal, Canada, Polger holds a BA in Sociology from Concordia University (1999), an MLIS from the University of Western Ontario (2000), an MA in Sociology from University of Waterloo (2004), and a BEd in Adult Education from Brock University (2009). He is currently a third-year PhD student in the Curriculum, Instruction, and the Science of Learning Program at SUNY University at Buffalo. He moved to New York City in 2008.The first issue of Marketing Libraries Journal was published in fall 2017. We wanted to ask Mark about his inspiration to create this new publication.—Editors
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".