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Record W1979937056 · doi:10.1002/bult.201

Architecture, Butterflies and Common Sense: The ABCs of a Profession on the Rise

2001· article· en· W1979937056 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBulletin of the American Society for Information Science and Technology · 2001
Typearticle
Languageen
FieldComputer Science
TopicInformation Architecture and Usability
Canadian institutionsnot available
Fundersnot available
KeywordsBallotCONTESTSummitWarrantThrivingCasualPolitical scienceMedia studiesHistorySociologyLawPoliticsGeographyCartographyVoting

Abstract

fetched live from OpenAlex

Since writing my first IA column (which appeared in the last issue of the Bulletin), much of note to the IAs among us has happened, including a successful, lively and very well attended symposium at La Jolla in November organized by Argus Associates (see the IA2000 pages at www.argus-acia.com for details and copies of presentations). As I write this, planning is well underway for a further ASIS summit on IA scheduled for San Francisco in February. Clearly the field is thriving, at least as far as events go. However, one issue dominated all others between columns and it would be remiss of me not to comment on the biggest information design issue of the last few years – the design of the ballot. THE ballot, the butterfly, that not-so-cleverly designed information space that may have changed the fate of a nation. I am sure many of you tired of the topic long before it left the news, but there are important lessons for IA in the design, analysis and media coverage of that issue and they warrant our attention as we seek to develop this field. Within hours of the non-result on November 7th, online discussion groups were dissecting the ballot's design, chat-shows were parodying its use and every usability guru online seemed to have an opinion on the ballot's readability, visual alignment and layout. On SIGIA-L it did not take long for the issue to morph into a broader debate about whether the issues surrounding that ballot and its use were really IA or usability concerns, with some members (correctly in my view) feeling that such a distinction was really not worth making, particularly in the face of such poor information practices. This issue of IA's distinctive role and focus surfaces repeatedly on SIGIA and I shall be devoting a future column to it, but for now let us agree that as information architects, we all feel we could have contributed usefully to the ballot and voting process design. I am not going to deal with the physical design of the ballot here other than to say that in my view the design was unnecessarily complex for what should be a simple task and, where errors were induced, they were systematic rather than random. Many postings eloquently articulated these issues in detail. What is more insightful I believe is the response to the design problem that has been witnessed from within the design professions, the broader public and the media. Practically everyone in the IA/HCI fields who commented on the design pointed to the same design flaws, though in the manner of all expert evaluations, the terms used to describe these flaws varied. No real surprises here but it begs the question of why such obvious flaws were never caught in advance. It has always been the case that design evaluations after the fact seem to show some 'obvious' errors that were somehow missed. Obvious or not (and I am inclined to think that even the present example contains a hint of folks being 'wise after the fact') a small dose of evaluation earlier on in the design process would have raised some red flags before the ballot was let loose. That all parties apparently examined and agreed to this design in advance only confirms what evaluation specialists have been saying for years – understanding the user response to a design is not just a matter of common sense. Evaluation needs to be built in to a design process and strong evaluation requires skilled professionals to conduct it. Common-sense examination by people not trained or well-versed in user issues is clearly not enough, no matter how motivated the inspector. If we learn nothing else from this episode, IAs and related professionals should drive this point home. This is only one important lesson however. Flawed information designs surround us but users often muddle through, perhaps losing some efficiency, surely suffering lower satisfaction from the interaction and inevitably completing tasks in a manner that is not ideal. Such is life, and users seem pretty tolerant of technologies if they are good enough for our purposes. In the voting scenario, however, the luxury of muddling though was denied many users. News accounts indicated that confused users were often denied a chance to correct their 'errors,' the context of use was manned by key stakeholders who allegedly provided no undo feature, a form of interaction that would rightly infuriate any user of current information technology. Beyond the physical interface to any information space lurks a context of use which can shape and influence acceptance and use. I have not heard many in the IA communities discuss this issue when analyzing the ballot design. Could this be a further consequence of narrowing our view of what is really within the remit of IAs and what lies without? Beyond the professional interest groups, the ballot design was much discussed and dissected. The Sunday edition of the New York Times immediately after the election contained an article on the design's impact on voters and even presented an improved version proposed within hours by a local design company. Such media coverage of design issues is rare and in many ways we should be grateful for the exposure provided. User confusion, usability, readability and user-centered design were common phrases for awhile in news reports and for a brief few days there was a 'call-to-arms' thread in relevant lists urging interaction designers and IA's to make their voices heard. The Usability Professionals Association even issued a public statement on the topic (see www.upassoc.org) claiming that the problems could easily have been avoided. But just how easy would it have been? Making others aware of just what we do as professionals has always proved more difficult than it would appear. Most media commentators felt the design problems were obvious (and did anyone articulate anything to contradict that?), and a well known NPR journalist was quoted as saying that when it came to voter errors, there was no accounting for user stupidity. Sound familiar? Evaluation is an exercise in common sense and if errors occur it is probably the user's fault! Is this the message we really want people to take from this windfall of publicity? If the problems were so simple to cure, who needs IAs? The limitations of the dialog on the issue were never more apparent than the treatment given to the statistical analyses of voter patterns which purported to show that the distribution of votes in locations with the questionable ballot were highly improbable. But you can prove anything with statistics, can't you? In comparison to the layout issue, the statisticians were barely heard, never mind understood. Results from a Canadian test indicated that the ballot design really did make it harder to vote for a certain candidate. This study, conducted within days of the election, was hungrily consumed by the newsgroups and the press, where no doubt many readers wished they had thought of doing it first. But why didn't anyone ask how valid this was? Where does one find participants in that window of time unsullied by the news from Florida? There are some important lessons to learn, and they require us to educate the public more on what we can offer by way of design. A little less concern with defining such issues in or out of IA, and more with showing a unified front on the design issues that matter to real people, would be a start. IA is about design, and design impacts people, at the task level, the context level and the social level. Understanding and predicting this impact is not simply a matter of common sense, but a matter of theory and method. Before the memories of the butterfly ballot fade, we would do well to remind politicians, working groups on election reform and the media of this. In so doing, IAs must work on improving our own communication of these ideas to make them more comprehensible to the rest of the world.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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