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Record W7033646563

Research information management (draft, to be published in the Digital Information Review #2 series, by Chandos Publishing.)

2013· other· en· W7033646563 on OpenAlexaboutno aff

Bibliographic record

VenueJISC Information Environment Repository (Jisc) · 2013
Typeother
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsnot available
Fundersnot available
KeywordsDebtContext (archaeology)Information technologyPie chartPublic sectorDigital economySubject (documents)Public policy
DOInot available

Abstract

fetched live from OpenAlex

Academic research is increasingly subject to managerial practices. It is not the purpose of this chapter to debate the merits, or otherwise, of this trend. Instead, this chapter will chart some of the major developments in research information management in the UK, Europe, the USA, Canada and Australia, and their policy context and drivers. In doing so, it will identify some of the key challenges and points of discussion in 2013.
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\nThe main challenges faced by universities and other research institutes are related to changes in the wider economy and society, and these might be considered under three headings:
\n1. Debt: Many, but not all, western governments are saddled with very high levels of debt and are responding by restricitions in public spending. In some countries such as the UK, the research budget has been relatively protected. However, there remains a drive to reduce public spending as a proportion of GDP, and this is likely to impact on research funding sooner or later. As the pressure to delivery more with less increases, the opportunities to do so also increase because of developments in new digital and other techniologies. There are therefore both drivers and opportunities for greater efficiencies in the research process.
\n2. Growth: Whether growth is measured in purely financial terms or in more rounded metrics that take into account human and environmental well-being, many western countries are in need of more of it to both reduce debt and ensure social stability. There is renewed attention on the role of the public research base in contributing to (having an "impact" on) the wider economy and society. There is a complicated and necessarily ill-defined relationship between research and society, but it is clear that there are increasing calls from politicians (and therefore from public funders) for researchers to demonstrate the impact of their work, 3. Legitimacy. For various reasons, including the current "impact agenda", questions are raised as to the legitimacy of research, both as a recipient of public and charitable funds and, more basically, at an epistemological level. Certainly there are increasing calls for transparency in research, both with respect to how the money is spent (in common with wider moves in that direction), and with respect to research practices being open to scrutiny and demonstrably better than other alternative approaches that make knowledge claims.
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\nThere are therefore three strong agendas within research, which shape the ways in which research is managed, and therefore the information infrastructure that is needed. These are efficiency, impact and transparency.
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\nIt is possible to discern two broad responses by the research community to these agendas, These are moves to make research more open, and increasing managerialism in research.
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\nThere is a lot of talk about "open" at the moment; open access, open data, open source software, open science, and so on. While there are some commonalitiies between these (they all challenge existing business models, for example, and they all exploit the web), there are also important differences. From a research management perspective, they challenge universities and research institutes to provide their researchers with an environment in which they can be open, if they choose to be. This implies the provision of open access repositories, funds to pay open access publication charges, data repositories, IT services departments able to both provide reslient compute infrastructure and cooperate wih researchers writing or using open source tools, and information, IT, library, publication, knowledge transfer and IPR policies that support open approaches. UK universities such as Nottingham and Glasgow have been very active in addressing these challenges, as have universities elsewhere such as Arizona and Queensland.
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\nSometimes associated with open approaches, but sometimes in tension with them, there is a trend toward greater managerial accountability within public sector research. Many universities are implementing integrated research management systems, covering both pre- and post-award workflows, and linked to research information systems, repositories and so on. There are also international initiatives, such as ORCID for researcher identifiers, which are a response to widely felt inefficiencies in the system at the moment, from grant applications to journal manuscript submission. Furthermore, the HE sector is learning from large private sector corporations such as supermarkets and phone companies, that user data is key to business intelligence to guide successful organisations. Information about what its researchers are doing, with whom, to what effect, will be important to those charged with ensuring that public sector and charity-funded is successful, in the same way as it is important to private industry such as pharmaceutical companies. National research assessment exercises and the growth of commercial services such as Elsevier's SciVal are evidence of this.
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\nIn conclusion, research management, and the gathering, curation and exploitation of information about an increasingly open research process, are more and more central to the success of research. In the US, the phrase is "science of science policy", reflected in the STAR-Metrics initiative that covers some 90 top level US universities. This is intended to provide policy makers and research directors with data that can be treated with scientific methods to optimise the investments made in research. While this has taken things further than perhaps elsewhere, it represents a widespread trend; expect it to impact on your university soon.
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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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0100.037
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.008

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.

Opus teacher head0.012
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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